ieee final year projects 2011-2012 :: data mining:: elysium technologies pvt ltd

Elysium Technologies Private Limited ISO 9001:2008 A leading Research and Development Division Madurai | Chennai | Trichy | Coimbatore | Kollam| Singapore Website: elysiumtechnologies.com, elysiumtechnologies.info Email: [email protected]

IEEE Final Year Project List 2011-2012

Madurai Elysium Technologies Private Limited

230, Church Road, Annanagar,

Madurai , Tamilnadu – 625 020.

Contact : 91452 4390702, 4392702, 4394702.

eMail: [email protected]

Trichy Elysium Technologies Private Limited

3rd

Floor,SI Towers,

15 ,Melapudur , Trichy,

Tamilnadu – 620 001.

Contact : 91431 - 4002234.


Kollam Elysium Technologies Private Limited

Surya Complex,Vendor junction,

kollam,Kerala – 691 010.

Contact : 91474 2723622.


A b s t r a c t DATA ENGINEERING 2011 - 2012

01 Dual Framework and Algorithms for Targeted Online Data Delivery

A variety of emerging online data delivery applications challenge existing techniques for data delivery to human users,

applications, or middleware that are accessing data from multiple autonomous servers. In this paper, we develop a

framework for formalizing and comparing pull-based solutions and present dual optimization approaches. The first

approach, most commonly used nowadays, maximizes user utility under the strict setting of meeting a priori constraints

on the usage of system resources. We present an alternative and more flexible approach that maximizes user utility by

satisfying all users. It does this while minimizing the usage of system resources. We discuss the benefits of this latter

approach and develop an adaptive monitoring solution Satisfy User Profiles (SUPs). Through formal analysis, we identify

sufficient optimality conditions for SUP. Using real (RSS feeds) and synthetic traces, we empirically analyze the behavior

of SUP under varying conditions. Our experiments show that we can achieve a high degree of satisfaction of user utility

when the estimations of SUP closely estimate the real event stream, and has the potential to save a significant amount of

system resources. We further show that SUP can exploit feedback to improve user utility with only a moderate increase in

resource utilization.

02 A Flexible Data and Sensor A Fast Multiple Longest Common Subsequence (MLCS) Algorithm

How to achieve a flexible data and sensor planning service to schedule, plan, and empower diverse sensors and

heterogeneous data ordering systems is a big challenge. In this paper, a service-oriented framework of data and sensor

planning service for virtual sensors is proposed. The framework includes an Open Geospatial Consortium (OGC)-compliant

Sensor Planning Service (SPS), a Web Notification Service (WNS), a Sensor Observation Service (SOS), and virtual sensors.

There are two important key technologies in this framework, namely a flexible SPS middleware and an asynchronous

message notification mechanism. The flexible SPS middleware, based on a configuration file and standard interfaces, is

adopted to integrate virtual sensors into a sensor Web. A WNS-based asynchronous notification middleware is used to

inform the user of the status of a task that may need midterm or long-term actions. The framework has been successfully

demonstrated in application scenarios for Simplified General Perturbations Satellite Orbit Model 4 (SGP4) and Earth

Observation System Clearing HOuse (ECHO). The results show that the proposed method has the following improvements

over the existing SPS implementation: a uniform planning service for more satellites, a seamless connection with data

order systems, and a flexible service-oriented framework for virtual sensors.

03 A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification

Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this

paper, we propose a fuzzy similarity-based self-constructing algorithm for feature clustering. The words in the

feature vector of a document set are grouped into clusters, based on similarity test. Words that are similar to each

other are grouped into the same cluster. Each cluster is characterized by a membership function with statistical

mean and deviation. When all the words have been fed in, a desired number of clusters are formed automatically. We

then have one extracted feature for each cluster. The extracted feature, corresponding to a cluster, is a weighted

combination of the words contained in the cluster. By this algorithm, the derived membership functions match

closely with and describe properly the real distribution of the training data. Besides, the user need not specify the

number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted

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features can then be avoided. Experimental results show that our method can run faster and obtain better extracted

features than other methods.

04 A Generic Multilevel Architecture for Time Series Prediction

Rapidly evolving businesses generate massive amounts of time-stamped data sequences and cause a demand for both

univariate and multivariate time series forecasting. For such data, traditional predictive models based on autoregression

are often not sufficient to capture complex nonlinear relationships between multidimensional features and the time series

outputs. In order to exploit these relationships for improved time series forecasting while also better dealing with a wider

variety of prediction scenarios, a forecasting system requires a flexible and generic architecture to accommodate and tune

various individual predictors as well as combination methods. In reply to this challenge, an architecture for combined,

multilevel time series prediction is proposed, which is suitable for many different universal regressors and combination

methods. The key strength of this architecture is its ability to build a diversified ensemble of individual predictors that form

an input to a multilevel selection and fusion process before the final optimized output is obtained. Excellent generalization

ability is achieved due to the highly boosted complementarity of individual models further enforced through cross-

validation-linked training on exclusive data subsets and ensemble output postprocessing. In a sample configuration with

basic neural network predictors and a mean combiner, the proposed system has been evaluated in different scenarios and

showed a clear prediction performance gain.

05 A Link Analysis Extension of Correspondence Analysis for Mining Relational Databases

This work introduces a link analysis procedure for discovering relationships in a relational database or a graph,

generalizing both simple and multiple correspondence analysis. It is based on a random walk model through the

database defining a Markov chain having as many states as elements in the database. Suppose we are interested in

analyzing the relationships between some elements (or records) contained in two different tables of the relational

database. To this end, in a first step, a reduced, much smaller, Markov chain containing only the elements of interest

and preserving the main characteristics of the initial chain, is extracted by stochastic complementation [41]. This

reduced chain is then analyzed by projecting jointly the elements of interest in the diffusion map subspace [42] and

visualizing the results. This two-step procedure reduces to simple correspondence analysis when only two tables are

defined, and to multiple correspondence analysis when the database takes the form of a simple star-schema. On the

other hand, a kernel version of the diffusion map distance, generalizing the basic diffusion map distance to directed

graphs, is also introduced and the links with spectral clustering are discussed. Several data sets are analyzed by

using the proposed methodology, showing the usefulness of the technique for extracting relationships in relational

databases or graphs.

06 A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in

personalized web information gathering. However, when representing user profiles, many models have utilized only

knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is

proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from

both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against

benchmark models in web information gathering. The results show that this ontology model is successful

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07 Adaptive Cluster Distance Bounding for High-Dimensional Indexing

We consider approaches for similarity search in correlated, high-dimensional data sets, which are derived within a

clustering framework. We note that indexing by “vector approximation” (VA-File), which was proposed as a

technique to combat the “Curse of Dimensionality,” employs scalar quantization, and hence necessarily ignores

dependencies across dimensions, which represents a source of suboptimality. Clustering, on the other hand,

exploits interdimensional correlations and is thus a more compact representation of the data set. However, existing

methods to prune irrelevant clusters are based on bounding hyperspheres and/or bounding rectangles, whose lack

of tightness compromises their efficiency in exact nearest neighbor search. We propose a new cluster-adaptive

distance bound based on separating hyperplane boundaries of Voronoi clusters to complement our cluster based

index. This bound enables efficient spatial filtering, with a relatively small preprocessing storage overhead and is

applicable to euclidean and Mahalanobis similarity measures. Experiments in exact nearest-neighbor set retrieval,

conducted on real data sets, show that our indexing method is scalable with data set size and data dimensionality

and outperforms several recently proposed indexes. Relative to the VA-File, over a wide range of quantization

resolutions, it is able to reduce random IO accesses, given (roughly) the same amount of sequential IO operations,

by factors reaching 100X and more.

08 Anonymous Publication of Sensitive Transactional Data

Existing research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to

enforce privacy-preserving paradigms, such as k-anonymity and ‘-diversity, while minimizing the information loss incurred

in the anonymizing process (i.e., maximize data utility). Existing techniques work well for fixed-schema data, with low

dimensionality. Nevertheless, certain applications require privacy-preserving publishing of transactional data (or basket

data), which involve hundreds or even thousands of dimensions, rendering existing methods unusable. We propose two

categories of novel anonymization methods for sparse high-dimensional data. The first category is based on approximate

nearest-neighbor (NN) search in high-dimensional spaces, which is efficiently performed through locality-sensitive hashing

(LSH). In the second category, we propose two data transformations that capture the correlation in the underlying data: 1)

reduction to a band matrix and 2) Gray encoding-based sorting. These representations facilitate the formation of

anonymized groups with low information loss, through an efficient linear-time heuristic. We show experimentally, using

real-life data sets, that all our methods clearly outperform existing state of the art. Among the proposed techniques, NN-

search yields superior data utility compared to the band matrix transformation, but incurs higher computational overhead.

The data transformation based on Gray code sorting performs best in terms of both data utility and execution time.

09 Answering Frequent Probabilistic Inference Queries in Databases

Existing solutions for probabilistic inference queries mainly focus on answering a single inference query, but seldom

address the issues of efficiently returning results for a sequence of frequent queries, which is more popular and

practical in many real applications. In this paper, we mainly study the computation caching and sharing among a

sequence of inference queries in databases. The clique tree propagation (CTP) algorithm is first introduced in

databases for probabilistic inference queries. We use the materialized views to cache the intermediate results of the

previous inference queries, which might be shared with the following queries, and consequently reduce the time

cost. Moreover, we take the query workload into account to identify the frequently queried variables. To optimize

probabilistic inference queries with CTP, we cache these frequent query variables into the materialized views to

maximize the reuse. Due to the existence of different query plans, we present heuristics to estimate costs and select

the optimal query plan. Finally, we present the experimental evaluation in relational databases to illustrate the validity

and superiority of our approaches in answering frequent probabilistic inference queries.

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10 Authenticated Multistep Nearest Neighbor Search

Multistep processing is commonly used for nearest neighbor (NN) and similarity search in applications involving

highdimensional data and/or costly distance computations. Today, many such applications require a proof of result

correctness. In this setting, clients issue NN queries to a server that maintains a database signed by a trusted authority.

The server returns the NN set along with supplementary information that permits result verification using the data set

signature. An adaptation of the multistep NN algorithm incurs prohibitive network overhead due to the transmission of false

hits, i.e., records that are not in the NN set, but are nevertheless necessary for its verification. In order to alleviate this

problem, we present a novel technique that reduces the size of each false hit. Moreover, we generalize our solution for a

distributed setting, where the database is horizontally partitioned over several servers. Finally, we demonstrate the

effectiveness of the proposed solutions with real data sets of various dimensionalities.

11 Automatic Discovery of Personal Name Aliases from the Web

An individual is typically referred by numerous name aliases on the web. Accurate identification of aliases of a given

person name is useful in various web related tasks such as information retrieval, sentiment analysis, personal name

disambiguation, and relation extraction. We propose a method to extract aliases of a given personal name from the

web. Given a personal name, the proposed method first extracts a set of candidate aliases. Second, we rank the

extracted candidates according to the likelihood of a candidate being a correct alias of the given name. We propose a

novel, automatically extracted lexical pattern-based approach to efficiently extract a large set of candidate aliases

from snippets retrieved from a web search engine. We define numerous ranking scores to evaluate candidate aliases

using three approaches: lexical pattern frequency, word co-occurrences in an anchor text graph, and page counts on

the web. To construct a robust alias detection system, we integrate the different ranking scores into a single ranking

function using ranking support vector machines. We evaluate the proposed method on three data sets: an English

personal names data set, an English place names data set, and a Japanese personal names data set. The proposed

method outperforms numerous baselines and previously proposed name alias extraction methods, achieving a

statistically significant mean reciprocal rank (MRR) of 0.67. Experiments carried out using location names and

Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different

types of named entities, and for different languages. Moreover, the aliases extracted using the proposed method are

successfully utilized in an information retrieval task and improve recall by 20 percent in a relationdetection task.

12 Geospatial Automatic Enrichment of Semantic Relation Network and Its Application to Word Sense Disambiguation

The most fundamental step in semantic information processing (SIP) is to construct knowledge base (KB) at the human

level; that is to the general understanding and conception of human knowledge. WordNet has been built to be the most

systematic and as close to the human level and is being applied actively in various works. In one of our previous research,

we found that a semantic gap exists between concept pairs of WordNet and those of real world. This paper contains a study

on the enrichment method to build a KB. We describe the methods and the results for the automatic enrichment of the

semantic relation network. A rule based method using WordNet’s glossaries and an inference method using axioms for

WordNet relations are applied for the enrichment and an enriched WordNet (E-WordNet) is built as the result. Our

experimental results substantiate the usefulness of E-WordNet. An evaluation by comparison with the human level is

attempted. Moreover, WSD-SemNet, a new word sense disambiguation (WSD) method in which E-WordNet is applied, is

proposed and evaluated by comparing it with the state-of-the-art algorithm..

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13 Branch-and-Bound for Model Selection and Its Computational Complexity

Branch-and-bound methods are used in various data analysis problems, such as clustering, seriation and feature

selection. Classical approaches of branch-and-bound based clustering search through combinations of various

partitioning possibilities to optimize a clustering cost. However, these approaches are not practically useful for

clustering of image data where the size of data is large. Additionally, the number of clusters is unknown in most of

the image data analysis problems. By taking advantage of the spatial coherency of clusters, we formulate an

innovative branch-and-bound approach, which solves clustering problem as a model-selection problem. In this

generalized approach, cluster parameter candidates are first generated by spatially coherent sampling. A branch-

andbound search is carried out through the candidates to select an optimal subset. This paper formulates this

approach and investigates its average computational complexity. Improved clustering quality and robustness to

outliers compared to conventional iterative approach are demonstrated with experiments.

14 Measuring Client-Perceived Pageview Response Time of Internet Services

As e-commerce services are exponentially growing, businesses need quantitative estimates of client-perceived response

times to continuously improve the quality of their services. Current server-side nonintrusive measurement techniques are

limited to nonsecured HTTP traffic. In this paper, we present the design and evaluation a monitor, namely sMonitor, which

is able to measure client-perceived response times for both HTTP and HTTPS traffic. At the heart of sMonitor is a novel

size-based analysis method that parses live packets to delimit different webpages and to infer their response times. The

method is based on the observation that most HTTP(S)-compatible browsers send significantly larger requests for

container objects than those for embedded objects. sMonitor is designed to operate accurately in the presence of

complicated browser behaviors, such as parallel downloading of multiple webpages and HTTP pipelining, as well as packet

losses and delays. It requires only to passively collect network traffic in and out of the monitored secured services. We

conduct comprehensive experiments across a wide range of operating conditions using live secured Internet services, on

the PlanetLab, and on controlled networks. The experimental results demonstrate that sMonitor is able to control the

estimation error within 6.7 percent, in comparison with the actual measured time at the client side.

15 Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel

class. We address this issue and propose a data stream classification technique that integrates a novel class

detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels

of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of

concept-drift, when the underlying data distributions evolve in streams. In order to determine whether an instance

belongs to a novel class, the classification model sometimes needs to wait for more test instances to discover

similarities among those instances. A maximum allowable wait time Tc is imposed as a time constraint to classify a

test instance. Furthermore, most existing stream classification approaches assume that the true label of a data point

can be accessed immediately after the data point is classified. In reality, a time delay Tl is involved in obtaining the

true label of a data point since manual labeling is time consuming. We show how to make fast and correct

classification decisions under these constraints and apply them to real benchmark data. Comparison with state-of-

the-art stream classification techniques proves the superiority of our approach.

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16 Classification Using Streaming Random Forests

We consider the problem of data stream classification, where the data arrive in a conceptually infinite stream, and the

opportunity to examine each record is brief. We introduce a stream classification algorithm that is online, running in

amortized Oð1Þ time, able to handle intermittent arrival of labeled records, and able to adjust its parameters to respond to

changing class boundaries (“concept drift”) in the data stream. In addition, when blocks of labeled data are short, the

algorithm is able to judge internally whether the quality of models updated from them is good enough for deployment on

unlabeled records, or whether further labeled records are required. Unlike most proposed stream-classification algorithms,

multiple target classes can be handled. Experimental results on real and synthetic data show that accuracy is comparable

to a conventional classification algorithm that sees all of the data at once and is able to make multiple passes over it.

17 CoFiDS: A Belief-Theoretic Approach for Automated Collaborative Filtering

Automated Collaborative Filtering (ACF) refers to a group of algorithms used in recommender systems, a research topic

that has received considerable attention due to its e-commerce applications. However, existing techniques are rarely

capable of dealing with imperfections in user-supplied ratings. When such imperfections (e.g., ambiguities) cannot be

avoided, designers resort to simplifying assumptions that impair the system’s performance and utility. We have developed

a novel technique referred to as CoFiDS—Collaborative Filtering based on Dempster-Shafer belief-theoretic framework—

that can represent a wide variety of data imperfections, propagate them throughout the decision-making process without

the need to make simplifying assumptions, and exploit contextual information. With its DS-theoretic predictions, the

domain expert can either obtain a “hard” decision or can narrow the set of possible predictions to a smaller set. With its

capability to handle data imperfections, CoFiDS widens the applicability of ACF to such critical and sensitive domains as

medical decision support systems and defense-related applications. We describe the theoretical foundation of the system

and report experiments with a benchmark movie data set. We explore some essential aspects of CoFiDS’ behavior and

show that its performance compares favorably with other ACF systems

18 Collaborative Filtering with Personalized Skylines

Collaborative filtering (CF) systems exploit previous ratings and similarity in user behavior to recommend the top-k objects/

records which are potentially most interesting to the user assuming a single score per object. However, in various

applications, a record (e.g., hotel) maybe rated on several attributes (value, service, etc.), in which case simply returning the

ones with the highest overall scores fails to capture the individual attribute characteristics and to accommodate different

selection criteria. In order to enhance the flexibility of CF, we propose Collaborative Filtering Skyline (CFS), a general

framework that combines the advantages of CF with those of the skyline operator. CFS generates a personalized skyline for

each user based on scores of other users with similar behavior. The personalized skyline includes objects that are good on

certain aspects, and eliminates the ones that are not interesting on any attribute combination. Although the integration of

skylines and CF has several attractive properties, it also involves rather expensive computations. We face this challenge

through a comprehensive set of algorithms and optimizations that reduce the cost of generating personalized skylines. In

addition to exact skyline processing, we develop an approximate method that provides error guarantees. Finally, we

propose the top-k personalized skyline, where the user specifies the required output cardinality

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19 Comprehensive Citation Index for Research Networks

The existing Science Citation Index only counts direct citations, whereas PageRank disregards the number of direct

citations. We propose a new Comprehensive Citation Index (CCI) that evaluates both direct and indirect intellectual

influence of research papers, and show that CCI is more reliable in discovering research papers with far-reaching

influence.

20 Constrained Skyline Query Processing against Distributed Data.

The skyline of a multidimensional point set is a subset of interesting points that are not dominated by others. In this paper,

we investigate constrained skyline queries in a large-scale unstructured distributed environment, where relevant data are

distributed among geographically scattered sites. We first propose a partition algorithm that divides all data sites into

incomparable groups such that the skyline computations in all groups can be parallelized without changing the final result.

We then develop a novel algorithm framework called PaDSkyline for parallel skyline query processing among partitioned

site groups. We also employ intragroup optimization and multifiltering technique to improve the skyline query processes

within each group. In particular, multiple (local) skyline points are sent together with the query as filtering points, which

help identify unqualified local skyline points early on a data site. In this way, the amount of data to be transmitted via

network connections is reduced, and thus, the overall query response time is shortened further. Cost models and heuristics

are proposed to guide the selection of a given number of filtering points from a superset. A costefficient model is

developed to determine how many filtering points to use for a particular data site.

21 Continuous Monitoring of Distance-Based Range Queries

Given a positive value r, a distance-based range query returns the objects that lie within the distance r of the query

location. In this paper, we focus on the distance-based range queries that continuously change their locations in a

euclidean space. We present an efficient and effective monitoring technique based on the concept of a safe zone.

The safe zone of a query is the area with a property that while the query remains inside it, the results of the query

remain unchanged. Hence, the query does not need to be reevaluated unless it leaves the safe zone. Our

contributions are as follows: 1) We propose a technique based on powerful pruning rules and a unique access order

which efficiently computes the safe zone and minimizes the I/O cost. 2) We theoretically determine and

experimentally verify the expected distance a query moves before leaving the safe zone and, for majority of queries,

the expected number of guard objects. 3) Our experiments demonstrate that the proposed approach is close to

optimal and is an order of magnitude faster than a naı¨ve algorithm. 4) We also extend our technique to monitor the

queries in a road network. Our algorithm is up to two order of magnitude faster than a naı¨ve algorithm.

22 Cosdes: A Collaborative Spam Detection System with a Novel E-Mail Abstraction Scheme

The E-mail communication is indispensable nowadays, but the e-mail spam problem continues growing drastically. In

recent years, the notion of collaborative spam filtering with near-duplicate similarity matching scheme has been widely

discussed. The primary idea of the similarity matching scheme for spam detection is to maintain a known spam database,

formed by user feedback, to block subsequent near-duplicate spams. On purpose of achieving efficient similarity matching

and reducing storage utilization, prior works mainly represent each e-mail by a succinct abstraction derived from e-mail

content text. However, these abstractions of e-mails cannot fully catch the evolving nature of spams, and are thus not

effective enough in near-duplicate detection. In this paper, we propose a novel e-mail abstraction scheme, which considers

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e-mail layout structure to represent e-mails. We present a procedure to generate the e-mail abstraction using HTML content

in e-mail, and this newly devised abstraction can more effectively capture the near-duplicate phenomenon of spams.

Moreover, we design a complete spam detection system Cosdes (standing for COllaborative Spam DEtection System),

which possesses an efficient near-duplicate matching scheme and a progressive update scheme. The progressive update

scheme enables system Cosdes to keep the most up-to-date information for near-duplicate detection. We evaluate Cosdes

on a live data set collected from a real e-mail server and show that our system outperforms the prior approaches in

detection results and is applicable to the real world.

23 Coupling Logical Analysis of Data and Shadow Clustering for Partially Defined Positive Boolean Function Reconstruction

The problem of reconstructing the AND-OR expression of a partially defined positive Boolean function (pdpBf) is

solved by adopting a novel algorithm, denoted by LSC, which combines the advantages of two efficient techniques,

Logical Analysis of Data (LAD) and Shadow Clustering (SC). The kernel of the approach followed by LAD consists in

a breadth-first enumeration of all the prime implicants whose degree is not greater than a fixed maximum d. In

contrast, SC adopts an effective heuristic procedure for retrieving the most promising logical products to be

included in the resulting AND-OR expression. Since the computational cost required by LAD prevents its application

even for relatively small dimensions of the input domain, LSC employs a depth-first approach, with asymptotically

linear memory occupation, to analyze the prime implicants having degree not greater than d. In addition, the

theoretical analysis proves that LSC presents almost the same asymptotic time complexity as LAD. Extensive

simulations on artificial benchmarks validate the good behavior of the computational cost exhibited by LSC, in

agreement with the theoretical analysis. Furthermore, the pdpBf retrieved by LSC always shows a better

performance, in terms of complexity and accuracy, with respect to those obtained by LAD.

24 Data Leakage Detection

We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third

parties). Some of the data are leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop). The

distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been

independently gathered by other means. We propose data allocation strategies (across the agents) that improve the

probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In

some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and

identifying the guilty party.

25 Decision Trees for Uncertain Data

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers

to handle data with uncertain information. Value uncertainty arises in many applications during the data collection

process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple

repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but

by multiple values forming a probability distribution. Rather than abstracting uncertain data by statistical derivatives

(such as mean and median), we discover that the accuracy of a decision tree classifier can be much improved if the

“complete information” of a data item (taking into account the probability density function (pdf)) is utilized. We

extend classical decision tree building algorithms to handle data tuples with uncertain values. Extensive experiments

have been conducted which show that the resulting classifiers are more accurate than those using value averages.

Since processing pdfs is computationally more costly than processing single values (e.g., averages), decision tree

construction on uncertain data is more CPU demanding than that for certain data. To tackle this problem, we propose

a series of pruning techniques that can greatly improve construction efficiency.

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26 Design and Implementation of an Intrusion Response System for Relational Databases

The intrusion response component of an overall intrusion detection system is responsible for issuing a suitable response

to an anomalous request. We propose the notion of database response policies to support our intrusion response system

tailored for a DBMS. Our interactive response policy language makes it very easy for the database administrators to specify

appropriate response actions for different circumstances depending upon the nature of the anomalous request. The two

main issues that we address in context of such response policies are that of policy matching, and policy administration. For

the policy matching problem, we propose two algorithms that efficiently search the policy database for policies that match

an anomalous request. We also extend the PostgreSQL DBMS with our policy matching mechanism, and report

experimental results. The experimental evaluation shows that our techniques are very efficient. The other issue that we

address is that of administration of response policies to prevent malicious modifications to policy objects from legitimate

users. We propose a novel Joint Threshold Administration Model (JTAM) that is based on the principle of separation of

duty. The key idea in JTAM is that a policy object is jointly administered by at least k database administrator (DBAs), that is,

any modification made to a policy object will be invalid unless it has been authorized by at least k DBAs. We present design

details of JTAM which is based on a cryptographic threshold signature scheme, and show how JTAM prevents malicious

modifications to policy objects from authorized users. We also implement JTAM in the PostgreSQL DBMS, and report

experimental results on the efficiency of our techniques.

27 Differential Privacy via Wavelet Transforms

Privacy-preserving data publishing has attracted considerable research interest in recent years. Among the existing

solutions, ˇ-differential privacy provides the strongest privacy guarantee. Existing data publishing methods that

achieve ˇ-differential privacy, however, offer little data utility. In particular, if the output data set is used to answer

count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders

the results useless. In this paper, we develop a data publishing technique that ensures ˇ-differential privacy while

providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a

range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it.

We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical

analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data,

we show the effectiveness and efficiency of our solution.

28 Discovering Activities to Recognize and Track in a Smart Environment

The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for

providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to

monitor the functional health of smart home residents, we need to design technologies that recognize and track activities

that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the

approaches are applied to activities that have been preselected and for which labeled training data are available. In

contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in

an individual’s routine. With this capability, we can then track the occurrence of regular activities to monitor functional

health and to detect changes in an individual’s patterns and lifestyle. In this paper, we describe our activity mining and

tracking approach, and validate our algorithms on data collected in physical smart environments.

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29 Discovering Conditional Functional Dependencies

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension

of functional dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules

for cleaning relational data. However, finding quality CFDs is an expensive process that involves intensive manual

effort. To effectively identify data cleaning rules, we develop techniques for discovering CFDs from relations. Already

hard for traditional FDs, the discovery problem is more difficult for CFDs. Indeed, mining patterns in CFDs introduces

new challenges. We provide three methods for CFD discovery. The first, referred to as CFDMiner, is based on

techniques for mining closed item sets, and is used to discover constant CFDs, namely, CFDs with constant patterns

only. Constant CFDs are particularly important for object identification, which is essential to data cleaning and data

integration. The other two algorithms are developed for discovering general CFDs. One algorithm, referred to as

CTANE, is a levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other, referred to as

FastCFD, is based on the depth-first approach used in FastFD, a method for discovering FDs. It leverages closed-

item-set mining to reduce the search space. As verified by our experimental study, CFDMiner can be multiple orders

of magnitude faster than CTANE and FastCFD for constant CFD discovery. CTANE works well when a given relation

is large, but it does not scale well with the arity of the relation. FastCFD is far more efficient than CTANE when the

arity of the relation is large; better still, leveraging optimization based on closed-item-set mining, FastCFD also

scales well with the size of the relation. These algorithms provide a set of cleaning-rule discovery tools for users to

choose for different applications.

30 Effective Navigation of Query Results Based on Concept Hierarchies

Search queries on biomedical databases, such as PubMed, often return a large number of results, only a small

subset of which is relevant to the user. Ranking and categorization, which can also be combined, have been

proposed to alleviate this information overload problem. Results categorization for biomedical databases is the focus

of this work. A natural way to organize biomedical citations is according to their MeSH annotations. MeSH is a

comprehensive concept hierarchy used by PubMed. In this paper, we present the BioNav system, a novel search

interface that enables the user to navigate large number of query results by organizing them using the MeSH concept

hierarchy. First, the query results are organized into a navigation tree. At each node expansion step, BioNav reveals

only a small subset of the concept nodes, selected such that the expected user navigation cost is minimized. In

contrast, previous works expand the hierarchy in a predefined static manner, without navigation cost modeling. We

show that the problem of selecting the best concepts to reveal at each node expansion is NP-complete and propose

an efficient heuristic as well as a feasible optimal algorithm for relatively small trees. We show experimentally that

BioNav outperforms state-of-the-art categorization systems by up to an order of magnitude, with respect to the user

navigation cost.

31 Efficient Periodicity Mining in Time Series Databases Using Suffix Trees

Periodic pattern mining or periodicity detection has a number of applications, such as prediction, forecasting,

detection of unusual activities, etc. The problem is not trivial because the data to be analyzed are mostly noisy and

different periodicity types (namely symbol, sequence, and segment) are to be investigated. Accordingly, we argue

that there is a need for a comprehensive approach capable of analyzing the whole time series or in a subsection of it

to effectively handle different types of noise (to a certain degree) and at the same time is able to detect different

types of periodic patterns; combining these under one umbrella is by itself a challenge. In this paper, we present an

algorithm which can detect symbol, sequence (partial), and segment (full cycle) periodicity in time series. The

algorithm uses suffix tree as the underlying data structure; this allows us to design the algorithm such that its

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worstcase complexity is Oðk:n2Þ, where k is the maximum length of periodic pattern and n is the length of the

analyzed portion (whole or subsection) of the time series. The algorithm is noise resilient; it has been successfully

demonstrated to work with replacement, insertion, deletion, or a mixture of these types of noise. We have tested the

proposed algorithm on both synthetic and real data from different domains, including protein sequences. The

conducted comparative study demonstrate the applicability and effectiveness of the proposed algorithm; it is

generally more time-efficient and noise-resilient than existing algorithms.

32 A Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns

Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable,

relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking

user’s feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of

iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and

inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback

(NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of

efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user

query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation

patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query

Expansion (QEX), to converge the search space toward the user’s intention effectively. By using NPRF method, high quality

of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF

outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.

33 Efficient Techniques for Online Record Linkage

The need to consolidate the information contained in heterogeneous data sources has been widely documented in

recent years. In order to accomplish this goal, an organization must resolve several types of heterogeneity problems,

especially the entity heterogeneity problem that arises when the same real-world entity type is represented using

different identifiers in different data sources. Statistical record linkage techniques could be used for resolving this

problem. However, the use of such techniques for online record linkage could pose a tremendous communication

bottleneck in a distributed environment (where entity heterogeneity problems are often encountered). In order to

resolve this issue, we develop a matching tree, similar to a decision tree, and use it to propose techniques that

reduce the communication overhead significantly, while providing matching decisions that are guaranteed to be the

same as those obtained using the conventional linkage technique. These techniques have been implemented, and

experiments with real-world and synthetic databases show significant reduction in communication overhead.

34 Efficient Top-k Approximate Subtree Matching in Small Memory

We consider the Top-k Approximate Subtree Matching (TASM) problem: finding the k best matches of a small query tree

within a large document tree using the canonical tree edit distance as a similarity measure between subtrees. Evaluating

the tree edit distance for large XML trees is difficult: the best known algorithms have cubic runtime and quadratic space

complexity, and, thus, do not scale. Our solution is TASM-postorder, a memory-efficient and scalable TASM algorithm. We

prove an upper bound for the maximum subtree size for which the tree edit distance needs to be evaluated. The upper

bound depends on the query and is independent of the document size and structure. A core problem is to efficiently prune

subtrees that are above this size threshold. We develop an algorithm based on the prefix ring buffer that allows us to prune

all subtrees above the threshold in a single postorder scan of the document. The size of the prefix ring buffer is linear in the

threshold. As a result, the space complexity of TASM-postorder depends only on k and the query size, and the runtime of

TASM-postorder is linear in the size of the document. Our experimental evaluation on large synthetic and real XML

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documents confirms our analytic results.

35 Energy Time Series Forecasting Based on Pattern Sequence Similarity

This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences.

First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the

prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is

extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the

samples immediately after the matched sequence. The main novelty is that only the labels associated with each

pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time

series until the last step of the prediction process. Results from several energy time series are reported and the

performance of the proposed method is compared to that of recently published techniques showing a remarkable

improvement in the prediction.

36 Energy Time Series Forecasting Based on Pattern Sequence Similarity

This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First,

clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a

data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this

sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after

the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the

future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction

process. Results from several energy time series are reported and the performance of the proposed method is compared to

that of recently published techniques showing a remarkable improvement in the prediction.

37 Estimating and Enhancing Real-Time Data Service Delays: Control-Theoretic Approaches

It is essential to process real-time data service requests such as stock quotes and trade transactions in a timely

manner using fresh data, which represent the current real-world phenomena such as the stock market status. Users

may simply leave when the database service delay is excessive. Also, temporally inconsistent data may give an

outdated view of the real-world status. However, supporting the desired timeliness and freshness is challenging due

to dynamic workloads. To address the problem, we present new approaches for 1) database backlog estimation, 2)

fine-grained closed-loop admission control based on the backlog model, and 3) incoming load smoothing. Our

backlog estimation and control-theoretic approaches aim to support the desired service delay bound without

degrading the data freshness, critical for real-time data services. Specifically, we design, implement, and evaluate

two feedback controllers based on linear control theory and fuzzy logic control theory, to meet the desired service

delay. Workload smoothing, under overload, helps the database admit and process more transactions in a timely

fashion by probabilistically reducing the burstiness of incoming data service requests. In terms of the data service

delay and throughput, our closed-loop admission control and probabilistic load smoothing schemes considerably

outperform several baselines in the experiments undertaken in a stock trading database testbed.

38 Experience Transfer for the Configuration Tuning in Large-Scale Computing Systems

This paper proposes a new strategy, the experience transfer, to facilitate the management of large-scale computing

systems. It deals with the utilization of management experiences in one system (or previous systems) to benefit the same

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management task in other systems (or current systems). We use the system configuration tuning as a case application to

demonstrate all procedures involved in the experience transfer including the experience representation, experience

extraction, and experience embedding. The dependencies between system configuration parameters are treated as

transferable experiences in the configuration tuning for two reasons: 1) because such knowledge is helpful to the efficiency

of the optimal configuration search, and 2) because the parameter dependencies are typically unchanged between two

similar systems. We use the Bayesian network to model configuration dependencies and present a configuration tuning

algorithm based on the Bayesian network construction and sampling. As a result, after the configuration tuning is

completed in the original system, we can obtain a Bayesian network as the by-product which records the dependencies

between system configuration parameters. Such a network is then embedded into the tuning process in other similar

systems as transferred experiences to improve the configuration search efficiency. Experimental results in a web-based

system show that with the help of transferred experiences, the configuration tuning process can be significantly

accelerated.

39 Exploring Application-Level Semantics for Data Compression

This Natural phenomena show that many creatures form large social groups and move in regular patterns. However,

previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first

propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their

movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D,

which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression

algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a

Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction

phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal

solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental

results show that the proposed compression algorithm leverages the group movement patterns to reduce the

amount of delivered data effectively and efficiently.


Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous

works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an

efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in

wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group

movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an

entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the

location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR)

problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules

and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm

leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

41 Finding Correlated Biclusters from Gene Expression Data

Extracting biologically relevant information from DNA microarrays is a very important task for drug development and

test, function annotation, and cancer diagnosis. Various clustering methods have been proposed for the analysis of

gene expression data, but when analyzing the large and heterogeneous collections of gene expression data,

conventional clustering algorithms often cannot produce a satisfactory solution. Biclustering algorithm has been

presented as an alternative approach to standard clustering techniques to identify local structures from gene

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expression data set. These patterns may provide clues about the main biological processes associated with different

physiological states. In this paper, different from existing bicluster patterns, we first introduce a more general

pattern: correlated bicluster, which has intuitive biological interpretation. Then, we propose a novel transform

technique based on singular value decomposition so that identifying correlated-bicluster problem from gene

expression matrix is transformed into two global clustering problems. The Mixed-Clustering algorithm and the Lift

algorithm are devised to efficiently produce ˇ-corBiclusters. The biclusters obtained using our method from gene

expression data sets of multiple human organs and the yeast Saccharomyces cerevisiae demonstrate clear

biological meanings.

42 Frequent Item Computation on a Chip

This Computing frequent items is an important problem by itself and as a subroutine in several data mining algorithms. In

this paper, we explore how to accelerate the computation of frequent items using field-programmable gate arrays (FPGAs)

with a threefold goal: increase performance over existing solutions, reduce energy consumption over CPU-based systems,

and explore the design space in detail as the constraints on FPGAs are very different from those of traditional software-

based systems. We discuss three design alternatives, each one of them exploiting different FPGA features and each one

providing different performance/scalability trade-offs. An important result of the paper is to demonstrate how the inherent

massive parallelism of FPGAs can improve performance of existing algorithms but only after a fundamental redesign of the

algorithms. Our experimental results show that, e.g., the pipelined solution we introduce can reach more than 100 million

tuples per second of sustained throughput (four times the best available results to date) by making use of techniques that

are not available to CPU-based solutions. Moreover, and unlike in software approaches, the high throughput is independent

of the skew of the Zipf distribution of the input and at a far lower energy cost. paper presents a new approach to forecast

the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of

grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the

pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the

prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only

the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of

real values of the time series until the last step of the prediction process. Results from several energy time series are

reported and the performance of the proposed method is compared to that of recently published techniques showing a

remarkable improvement in the prediction.

43 Inconsistency-Tolerant Integrity Checking

All methods for efficient integrity checking require all integrity constraints to be totally satisfied, before any update is

executed. However, a certain amount of inconsistency is the rule, rather than the exception in databases. In this

paper, we close the gap between theory and practice of integrity checking, i.e., between the unrealistic theoretical

requirement of total integrity and the practical need for inconsistency tolerance, which we define for integrity

checking methods. We show that most of them can still be used to check whether updates preserve integrity, even if

the current state is inconsistent. Inconsistency-tolerant integrity checking proves beneficial both for integrity

preservation and query answering. Also, we show that it is useful for view updating, repairs, schema evolution, and

other applications.

44 Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or

approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which

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algorithms are effective for initialization? When should the search process be restarted? In the present work, we investigate

these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian

networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While

the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a

novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture

models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search

algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE

search time for application BNs by several orders of magnitude compared to using uniform at random initialization without

restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree

clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

45 Integration of the HL7 Standard in a Multiagent System to Support Personalized Access to e-Health Services

This Natural phenomena show that many creatures form large social groups and move in regular patterns. However,

previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first

propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their

movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D,

which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression

algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a

Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction

phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal

solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental

results show that the proposed compression algorithm leverages the group movement patterns to reduce the

amount of delivered data effectively and efficiently.


Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous

works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an

efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in

wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group

movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an

entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the

location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR)

problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules

and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm

leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

47 Intertemporal Discount Factors as a Measure of Trustworthiness in Electronic Commerce

In multiagent interactions, such as e-commerce and file sharing, being able to accurately assess the trustworthiness

of others is important for agents to protect themselves from losing utility. Focusing on rational agents in e-

commerce, we prove that an agent’s discount factor (time preference of utility) is a direct measure of the agent’s

trustworthiness for a set of reasonably general assumptions and definitions. We propose a general list of desiderata

for trust systems and discuss how discount factors as trustworthiness meet these desiderata. We discuss how

discount factors are a robust measure when entering commitments that exhibit moral hazards. Using an online

market as a motivating example, we derive some analytical methods both for measuring discount factors and for

aggregating the measurements.

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48 IR-Tree: An Efficient Index for Geographic Document Search

Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves

documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks

the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index

that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search

engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that

together with a top-k document search algorithm facilitates four major tasks in document searches, namely, 1) spatial

filtering, 2) textual filtering, 3) relevance computation, and 4) document ranking in a fully integrated manner. In addition, IR-

tree allows searches to adopt different weights on textual and spatial relevance of documents at the runtime and thus

caters for a wide variety of applications. A set of comprehensive experiments over a wide range of scenarios has been

conducted and the experiment results demonstrate that IR-tree outperforms the state-of-theart approaches for geographic

document searches.

49 Knowledge Discovery in Services (KDS): Aggregating Software Services to Discover Enterprise Mashups

Service mashup is the act of integrating the resulting data of two complementary software services into a common

picture. Such an approach is promising with respect to the discovery of new types of knowledge. However, before

service mashup routines can be executed, it is necessary to predict which services (of an open repository) are viable

candidates. Similar to Knowledge Discovery in Databases (KDD), we introduce the Knowledge Discovery in Services

(KDS) process that identifies mashup candidates. In this work, the KDS process is specialized to address a

repository of open services that do not contain semantic annotations. In these situations, specialized techniques are

required to determine equivalences among open services with reasonable precision. This paper introduces a bottom-

up process for KDS that adapts to the environment of services for which it operates. Detailed experiments are

discussed that evaluate KDS techniques on an open repository of services from the Internet and on a repository of

services created in a controlled environment.

50 Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction

Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis

(LDA) is a traditional algorithm for supervised feature extraction. Recently, unlabeled data have been utilized to improve

LDA. However, the intrinsic problems of LDA still exist and only the similarity among the unlabeled data is utilized. In this

paper, we propose a novel algorithm, called Semisupervised Semi-Riemannian Metric Map (S3RMM), following the

geometric framework of semi- Riemannian manifolds. S3RMM maximizes the discrepancy of the separability and similarity

measures of scatters formulated by using semi-Riemannian metric tensors. The metric tensor of each sample is learned via

semisupervised regression. Our method can also be a general framework for proposing new semisupervised algorithms,

utilizing the existing discrepancy-criterion-based algorithms. The experiments demonstrated on faces and handwritten

digits show that S3RMM is promising for semisupervised feature extraction.

51 Load Shedding in Mobile Systems with MobiQual

In location-based, mobile continual query (CQ) systems, two key measures of quality-of-service (QoS) are: freshness

and accuracy. To achieve freshness, the CQ server must perform frequent query reevaluations. To attain accuracy,

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the CQ server must receive and process frequent position updates from the mobile nodes. However, it is often

difficult to obtain fresh and accurate CQ results simultaneously, due to 1) limited resources in computing and

communication and 2) fast-changing load conditions caused by continuous mobile node movement. Hence, a key

challenge for a mobile CQ system is: How do we achieve the highest possible quality of the CQ results, in both

freshness and accuracy, with currently available resources? In this paper, we formulate this problem as a load

shedding one, and develop MobiQual—a QoS-aware approach to performing both update load shedding and query

load shedding. The design of MobiQual highlights three important features. 1) Differentiated load shedding: We apply

different amounts of query load shedding and update load shedding to different groups of queries and mobile nodes,

respectively. 2) Per-query QoS specification: Individualized QoS specifications are used to maximize the overall

freshness and accuracy of the query results. 3) Lowcost adaptation: MobiQual dynamically adapts, with a minimal

overhead, to changing load conditions and available resources. We conduct a set of comprehensive experiments to

evaluate the effectiveness of MobiQual. The results show that, through a careful combination of update and query

load shedding, the MobiQual approach leads to much higher freshness and accuracy in the query results in all cases,

compared to existing approaches that lack the QoS-awareness properties of MobiQual, as well as the solutions that

perform query-only or update-only load shedding.

52 Locally Consistent Concept Factorization for Document Clustering

For Previous studies have demonstrated that document clustering performance can be improved significantly in lower

dimensional linear subspaces. Recently, matrix factorization-based techniques, such as Nonnegative Matrix Factorization

(NMF) and Concept Factorization (CF), have yielded impressive results. However, both of them effectively see only the

global euclidean geometry, whereas the local manifold geometry is not fully considered. In this paper, we propose a new

approach to extract the document concepts which are consistent with the manifold geometry such that each concept

corresponds to a connected component. Central to our approach is a graph model which captures the local geometry of the

document submanifold. Thus, we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to

smooth the document-to-concept mapping, LCCF can extract concepts with respect to the intrinsic manifold structure and

thus documents associated with the same concept can be well clustered. The experimental results on TDT2 and Reuters-

21578 have shown that the proposed approach provides a better representation and achieves better clustering results in

terms of accuracy and mutual information.

53 Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments

Researches on Location-Based Service (LBS) have been emerging in recent years due to a wide range of potential

applications. One of the active topics is the mining and prediction of mobile movements and associated transactions.

Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns

may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal

periods are not considered. In this paper, we propose a novel algorithm, namely, Cluster-based Temporal Mobile

Sequential Pattern Mine (CTMSP-Mine), to discover the Cluster-based Temporal Mobile Sequential Patterns

(CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In CTMSP-Mine,

user clusters are constructed by a novel algorithm named Cluster-Object-based Smart Cluster Affinity Search

Technique (CO-Smart-CAST) and similarities between users are evaluated by the proposed measure, Location-Based

Service Alignment (LBS-Alignment). Meanwhile, a time segmentation approach is presented to find segmenting time

intervals where similar mobile characteristics exist. To our best knowledge, this is the first work on mining and

prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through

experimental evaluation under various simulated conditions, the proposed methods are shown to deliver excellent

performance.

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54 Mining Discriminative Patterns for Classifying Trajectories on Road Networks

Classification has been used for modeling many kinds of data sets, including sets of items, text documents, graphs, and

networks. However, there is a lack of study on a new kind of data, trajectories on road networks. Modeling such data is

useful with the emerging GPS and RFID technologies and is important for effective transportation and traffic planning. In

this work, we study methods for classifying trajectories on road networks. By analyzing the behavior of trajectories on road

networks, we observe that, in addition to the locations where vehicles have visited, the order of these visited locations is

crucial for improving classification accuracy. Based on our analysis, we contend that (frequent) sequential patterns are

good feature candidates since they preserve this order information. Furthermore, when mining sequential patterns, we

propose to confine the length of sequential patterns to ensure high efficiency. Compared with closed sequential patterns,

these partial (i.e., length-confined) sequential patterns allow us to significantly improve efficiency almost without losing

accuracy. In this paper, we present a framework for frequent pattern-based classification for trajectories on road networks.

Our comparative study over a broad range of classification approaches demonstrates that our method significantly

improves accuracy over other methods in some synthetic and real trajectory data.

47 Mining Group Movement Patterns for Tracking Moving Objects Efficiently

Existing object tracking applications focus on finding the moving patterns of a single object or all objects. In

contrast, we propose a distributed mining algorithm that identifies a group of objects with similar movement

patterns. This information is important in some biological research domains, such as the study of animals’ social

behavior and wildlife migration. The proposed algorithm comprises a local mining phase and a cluster ensembling

phase. In the local mining phase, the algorithm finds movement patterns based on local trajectories. Then, based on

the derived patterns, we propose a new similarity measure to compute the similarity of moving objects and identify

the local group relationships. To address the energy conservation issue in resource-constrained environments, the

algorithm only transmits the local grouping results to the sink node for further ensembling. In the cluster ensembling

phase, our algorithm combines the local grouping results to derive the group relationships from a global view. We

further leverage the mining results to track moving objects efficiently. The results of experiments show that the

proposed mining algorithm achieves good grouping quality, and the mining technique helps reduce the energy

consumption by reducing the amount of data to be transmitted.

48 Mining Iterative Generators and Representative Rules for Software Specification Discovery

Billions of dollars are spent annually on software-related cost. It is estimated that up to 45 percent of software cost is due to

the difficulty in understanding existing systems when performing maintenance tasks (i.e., adding features, removing bugs,

etc.). One of the root causes is that software products often come with poor, incomplete, or even without any documented

specifications. In an effort to improve program understanding, Lo et al. have proposed iterative pattern mining which

outputs patterns that are repeated frequently within a program trace, or across multiple traces, or both. Frequent iterative

patterns reflect frequent program behaviors that likely correspond to software specifications. To reduce the number of

patterns and improve the efficiency of the algorithm, Lo et al. have also introduced mining closed iterative patterns, i.e.,

maximal patterns without any superpattern having the same support. In this paper, to technically deepen research on

iterative pattern mining, we introduce mining iterative generators, i.e., minimal patterns without any subpattern having the

same support. Iterative generators can be paired with closed patterns to produce a set of rules expressing forward,

backward, and in-between temporal constraints among events in one general representation. We refer to these rules as

representative rules. A comprehensive performance study shows the efficiency of our approach. A case study on traces of

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an industrial system shows how iterative generators and closed iterative patterns can be merged to form useful rules

shedding light on software design.

49 Missing Value Estimation for Mixed-Attribute Data Sets

Service Missing data imputation is a key issue in learning from incomplete data. Various techniques have been

developed with great successes on dealing with missing values in data sets with homogeneous attributes (their

independent attributes are all either continuous or discrete). This paper studies a new setting of missing data

imputation, i.e., imputing missing data in data sets with heterogeneous attributes (their independent attributes are of

different types), referred to as imputing mixed-attribute data sets. Although many real applications are in this setting,

there is no estimator designed for imputing mixed-attribute data sets. This paper first proposes two consistent

estimators for discrete and continuous missing target values, respectively. And then, a mixture-kernelbased iterative

estimator is advocated to impute mixed-attribute data sets. The proposed method is evaluated with extensive

experiments compared with some typical algorithms, and the result demonstrates that the proposed approach is

better than these existing imputation methods in terms of classification accuracy and root mean square error (RMSE)

at different missing ratios

50 Monochromatic and Bichromatic Reverse Top-k Queries

Discriminant Nowadays, most applications return to the user a limited set of ranked results based on the individual user’s

preferences, which are commonly expressed through top-k queries. From the perspective of a manufacturer, it is imperative

that her products appear in the highest ranked positions for many different user preferences, otherwise the product is not

visible to potential customers. In this paper, we define a novel query type, namely the reverse top-k query, that covers this

requirement: “Given a potential product, which are the user preferences that make this product belong to the top-k query

result set?.” Reverse top-k queries are essential for manufacturers to assess the impact of their products in the market

based on the competition. We formally define reverse top-k queries and introduce two versions of the query,

monochromatic and bichromatic. First, we provide a geometric interpretation of the monochromatic reverse top-k query to

acquire an intuition of the solution space. Then, we study in detail the case of bichromatic reverse top-k query, and we

propose two techniques for query processing, namely an efficient threshold-based algorithm and an algorithm based on

materialized reverse top-k views. Our experimental evaluation demonstrates the efficiency of our techniques

51 On Computing Farthest Dominated Locations

In reality, spatial objects (e.g., hotels) not only have spatial locations but also have quality attributes (e.g., price,

star). An object p is said to dominate another one p0, if p is no worse than p0 with respect to every quality attribute

and p is better on at least one quality attribute. Traditional spatial queries (e.g., nearest neighbor, closest pair) ignore

quality attributes, whereas conventional dominance-based queries (e.g., skyline) neglect spatial locations. Motivated

by these observations, we propose a novel query by combining spatial and quality attributes together meaningfully.

Given a set of (competitors’) spatial objects P, a set of (candidate) locations L, and a quality vector as design

competence (for L), the farthest dominated location (FDL) query retrieves the location s 2 L such that the distance to

its nearest dominating object in P is maximized. FDL queries are suitable for various spatial decision support

applications such as business planning, wild animal protection, and digital battle field systems. As FDL queries

cannot be readily solved by existing techniques, we develop several efficient R-tree-based algorithms for processing

FDL queries, which offer users a range of selections in terms of different indexes available on the data. We also

generalize our methods to support the generic distance metric and other interesting query types. The experimental

results on both real and synthetic data sets disclose the performance of those algorithms, and reveal the most

efficient and scalable one among them.

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52 Optimizing Resource Conflicts in Workflow Management Systems

Resource allocation and scheduling are fundamental issues in a Workflow Management System (WfMS). Effective

resource management in WfMS should examine resource allocation together with task scheduling since these

problems impose mutual constraints. Optimization of the one factor is subject to the other constraints and vice

versa. Thus, an ideal algorithm should take into account not only performance metrics of the infrastructure, such as

the number of resources and their utilization, but also quality criteria such as the percentage of tasks undergone

violation in their temporal restrictions. In this paper, we propose an innovative algorithm which jointly optimizes the

two aforementioned contradictory criteria. The algorithm, called Resource Conflicts Joint Optimization

(Re.Co.Jo.Op.), minimizes resource conflicts subject to temporal constraints and simultaneously optimizes

throughput or utilization subject to resources constraints. To achieve the optimization, the two factors are formulated

in a matrix form and the optimal solution is found by applying concepts of the generalized eigenvalue analysis. A

rough outline of an agent-based architecture is proposed to achieve runtime integration of our algorithm into a

functional WfMS, while experimental results under different load environments and tasks assumption reveal the

superiority of the proposed strategy than the other conventional approaches.

53 Pareto-Based Dominant Graph: An Efficient Indexing Structure to Answer Top-K Queries

Given a record set D and a query score function F, a top-k query returns k records from D, whose values of function F on

their attributes are the highest. In this paper, we investigate the intrinsic connection between top-k queries and dominant

relationships between records, and based on which, we propose an efficient layer-based indexing structure, Pareto-Based

Dominant Graph (DG), to improve the query efficiency. Specifically, DG is built offline to express the dominant relationship

between records and top-k query is implemented as a graph traversal problem, i.e., Traveler algorithm. We prove

theoretically that the size of search space (that is the number of retrieved records from the record set to answer top-k

query) in our algorithm is directly related to the cardinality of skyline points in the record set (see Theorem 3). Considering

I/O cost, we propose cluster-based storage schema to reduce I/O cost in Traveler algorithm. We also propose the cost

estimation methods in this paper. Based on cost analysis, we propose an optimization technique, pseudorecord, to further

improve the search efficiency. In order to handle the top-k query in the high-dimension record set, we also propose N-Way

Traveler algorithm. In order to handle DG maintenance efficiently, we propose “Insertion” and “Deletion” algorithms for DG.

Finally, extensive experiments demonstrate that our proposed methods have significant improvement over its counterparts,

including both classical and state art of top-k algorithms.

54 Privacy-Preserving OLAP: An Information-Theoretic Approach

We address issues related to the protection of private information in Online Analytical Processing (OLAP) systems,

where a major privacy concern is the adversarial inference of private information from OLAP query answers. Most

previous work on privacypreserving OLAP focuses on a single aggregate function and/or addresses only exact

disclosure, which eliminates from consideration an important class of privacy breaches where partial information,

but not exact values, of private data is disclosed (i.e., partial disclosure). We address privacy protection against both

exact and partial disclosure in OLAP systems with mixed aggregate functions. In particular, we propose an

information-theoretic inference control approach that supports a combination of common aggregate functions (e.g.,

COUNT, SUM, MIN, MAX, and MEDIAN) and guarantees the level of privacy disclosure not to exceed thresholds

predetermined by the data owners. We demonstrate that our approach is efficient and can be implemented in existing

OLAP systems with little modification. It also satisfies the simulatable auditing model and leaks no private

information through query rejections. Through performance analysis, we show that compared with previous

approaches, our approach provides more effective privacy protection while maintaining a higher level of query-

answer availability.

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55 Ranking Spatial Data by Quality Preferences

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example,

using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the

appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital,

market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different

functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign

higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference

queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods

on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to

different parameters

56 RFID Data Processing in Supply Chain Management Using a Path Encoding Scheme

RFID technology can be applied to a broad range of areas. In particular, RFID is very useful in the area of business,

such as supply chain management. However, the amount of RFID data in such an environment is huge. Therefore,

much time is needed to extract valuable information from RFID data for supply chain management. In this paper, we

present an efficient method to process a massive amount of RFID data for supply chain management. We first define

query templates to analyze the supply chain. We then propose an effective path encoding scheme that encodes the

flows of products. However, if the flows are long, the numbers in the path encoding scheme that correspond to the

flows will be very large. We solve this by providing a method that divides flows. To retrieve the time information for

products efficiently, we utilize a numbering scheme for the XML area. Based on the path encoding scheme and the

numbering scheme, we devise a storage scheme that can process tracking queries and path oriented queries

efficiently on an RDBMS. Finally, we propose a method that translates the queries to SQL queries. Experimental

results show that our approach can process the queries efficiently.

57 Pareto-Based Dominant Graph: An Efficient Indexing Structure to Answer Top-K Queries

Given a record set D and a query score function F, a top-k query returns k records from D, whose values of function F on

their attributes are the highest. In this paper, we investigate the intrinsic connection between top-k queries and dominant

relationships between records, and based on which, we propose an efficient layer-based indexing structure, Pareto-Based

Dominant Graph (DG), to improve the query efficiency. Specifically, DG is built offline to express the dominant relationship

between records and top-k query is implemented as a graph traversal problem, i.e., Traveler algorithm. We prove

theoretically that the size of search space (that is the number of retrieved records from the record set to answer top-k

query) in our algorithm is directly related to the cardinality of skyline points in the record set (see Theorem 3). Considering

I/O cost, we propose cluster-based storage schema to reduce I/O cost in Traveler algorithm. We also propose the cost

estimation methods in this paper. Based on cost analysis, we propose an optimization technique, pseudorecord, to further

improve the search efficiency. In order to handle the top-k query in the high-dimension record set, we also propose N-Way

Traveler algorithm. In order to handle DG maintenance efficiently, we propose “Insertion” and “Deletion” algorithms for DG.

Finally, extensive experiments demonstrate that our proposed methods have significant improvement over its counterparts,

including both classical and state art of top-k algorithms.

58 Privacy-Preserving OLAP: An Information-Theoretic Approach

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59 Ranking Spatial Data by Quality Preferences

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example,

using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the

appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital,

market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different

functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign

higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference

queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods

on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to

different parameters

60 RFID Data Processing in Supply Chain Management Using a Path Encoding Scheme

RFID technology can be applied to a broad range of areas. In particular, RFID is very useful in the area of business,

such as supply chain management. However, the amount of RFID data in such an environment is huge. Therefore,

much time is needed to extract valuable information from RFID data for supply chain management. In this paper, we

present an efficient method to process a massive amount of RFID data for supply chain management. We first define

query templates to analyze the supply chain. We then propose an effective path encoding scheme that encodes the

flows of products. However, if the flows are long, the numbers in the path encoding scheme that correspond to the

flows will be very large. We solve this by providing a method that divides flows. To retrieve the time information for

products efficiently, we utilize a numbering scheme for the XML area. Based on the path encoding scheme and the

numbering scheme, we devise a storage scheme that can process tracking queries and path oriented queries

efficiently on an RDBMS. Finally, we propose a method that translates the queries to SQL queries. Experimental

results show that our approach can process the queries efficiently.

61 Seeking Quality of Web Service Composition in a Semantic Dimension






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62 Semantic Knowledge-Based Framework to Improve the Situation Awareness of Autonomous Underwater Vehicles

This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in

autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic

interoperability among all involved information sources. This will enhance interoperability, independence of operation, and

situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specifically

affect the mission flexibility, robustness, and autonomy. The presented framework makes use of the idea that

heterogeneous real-world data of very different type must be processed by (and run through) several different layers, to be

finally available in a suited format and at the right place to be accessible by high-level decision-making agents. In this

sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of

semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware

fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the status

monitoring agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can

interchange information while remaining domain independent during their interaction with the platform. The results of this

paper are readily applicable to land and air robotics.

63 Straggler Identification in Round-Trip Data Streams via Newton’s Identities and Invertible Bloom Filters

In this paper, we study the straggler identification problem, in which an algorithm must determine the identities of the

remaining members of a set after it has had a large number of insertion and deletion operations performed on it, and

now has relatively few remaining members. The goal is to do this in oðnÞ space, where n is the total number of

identities. Straggler identification has applications, for example, in determining the unacknowledged packets in a

high-bandwidth multicast data stream. We provide a deterministic solution to the straggler identification problem that

uses only Oðd log nÞ bits, based on a novel application of Newton’s identities for symmetric polynomials. This

solution can identify any subset of d stragglers from a set of nOðlog nÞ-bit identifiers, assuming that there are no

false deletions of identities not already in the set. Indeed, we give a lower bound argument that shows that any small-

space deterministic solution to the straggler identification problem cannot be guaranteed to handle false deletions.

Nevertheless, we provide a simple randomized solution, using Oðd log n logð1=ˇÞÞ bits that can maintain a multiset

and solve the straggler identification problem, tolerating false deletions, where ˇ > 0 is a user-defined parameter

bounding the probability of an incorrect response. This randomized solution is based on a new type of Bloom filter,

which we call the invertible Bloom filter.







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65 SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be

understood by human domain experts. Basically, time series are decomposed into short segments, and short-term trends

of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models.

Then, the classifiers assess short sequences of trends in subsequent segments with their rule premises. The conclusions

gradually assign an input to a class. As the classifier is a generative model of the processes from which the time series are

assumed to originate, anomalies can be detected, too. Segmentation and piecewise polynomial modeling are done

extremely fast in only one pass over the time series. Thus, the approach is applicable to problems with harsh timing

constraints. We lay the theoretical foundations for this classifier, including a new distance measure for time series and a

new technique to construct a dynamic classifier from a static one, and demonstrate its properties by means of various

benchmark time series, for example, Lorenz attractor time series, energy consumption in a building, or ECG data.

66 Temporal Data Clustering via Weighted Clustering Ensemble with Different Representations

Temporal data clustering provides underpinning techniques for discovering the intrinsic structure and condensing

information over temporal data. In this paper, we present a temporal data clustering framework via a weighted

clustering ensemble of multiple partitions produced by initial clustering analysis on different temporal data

representations. In our approach, we propose a novel weighted consensus function guided by clustering validation

criteria to reconcile initial partitions to candidate consensus partitions from different perspectives, and then,

introduce an agreement function to further reconcile those candidate consensus partitions to a final partition. As a

result, the proposed weighted clustering ensemble algorithm provides an effective enabling technique for the joint

use of different representations, which cuts the information loss in a single representation and exploits various

information sources underlying temporal data. In addition, our approach tends to capture the intrinsic structure of a

data set, e.g., the number of clusters. Our approach has been evaluated with benchmark time series, motion

trajectory, and time-series data stream clustering tasks. Simulation results demonstrate that our approach yields

favorite results for a variety of temporal data clustering tasks. As our weighted cluster ensemble algorithm can

combine any input partitions to generate a clustering ensemble, we also investigate its limitation by formal analysis

and empirical studies.









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69 Text Clustering with Seeds Affinity Propagation

Based on an effective clustering algorithm—Affinity Propagation (AP)—we present in this paper a novel

semisupervised text clustering algorithm, called Seeds Affinity Propagation (SAP). There are two main contributions

in our approach: 1) a new similarity metric that captures the structural information of texts, and 2) a novel seed

construction method to improve the semisupervised clustering process. To study the performance of the new

algorithm, we applied it to the benchmark data set Reuters-21578 and compared it to two state-of-the-art clustering

algorithms, namely, k-means algorithm and the original AP algorithm. Furthermore, we have analyzed the individual

impact of the two proposed contributions. Results show that the proposed similarity metric is more effective in text

clustering (F-measures ca. 21 percent higher than in the AP algorithm) and the proposed semisupervised strategy

achieves both better clustering results and faster convergence (using only 76 percent iterations of the original AP).

The complete SAP algorithm obtains higher F-measure (ca. 40 percent improvement over k-means and AP) and lower

entropy (ca. 28 percent decrease over k-means and AP), improves significantly clustering execution time (20 times

faster) in respect that k-means, and provides enhanced robustness compared with all other methods.

70 Temporal Data Clustering via Weighted Clustering Ensemble with Different Representations

Temporal data clustering provides underpinning techniques for discovering the intrinsic structure and condensing

information over temporal data. In this paper, we present a temporal data clustering framework via a weighted

clustering ensemble of multiple partitions produced by initial clustering analysis on different temporal data

representations. In our approach, we propose a novel weighted consensus function guided by clustering validation

criteria to reconcile initial partitions to candidate consensus partitions from different perspectives, and then,

introduce an agreement function to further reconcile those candidate consensus partitions to a final partition. As a

result, the proposed weighted clustering ensemble algorithm provides an effective enabling technique for the joint

use of different representations, which cuts the information loss in a single representation and exploits various

information sources underlying temporal data. In addition, our approach tends to capture the intrinsic structure of a

data set, e.g., the number of clusters. Our approach has been evaluated with benchmark time series, motion

trajectory, and time-series data stream clustering tasks. Simulation results demonstrate that our approach yields

favorite results for a variety of temporal data clustering tasks. As our weighted cluster ensemble algorithm can

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combine any input partitions to generate a clustering ensemble, we also investigate its limitation by formal analysis

and empirical studies.

71 TEXT: Automatic Template Extraction from Heterogeneous Web Pages

We World Wide Web is the most useful source of information. In order to achieve high productivity of publishing, the

webpages in many websites are automatically populated by using the common templates with contents. The

templates provide readers easy access to the contents guided by consistent structures. However, for machines, the

templates are considered harmful since they degrade the accuracy and performance of web applications due to

irrelevant terms in templates. Thus, template detection techniques have received a lot of attention recently to

improve the performance of search engines, clustering, and classification of web documents. In this paper, we

present novel algorithms for extracting templates from a large number of web documents which are generated from

heterogeneous templates. We cluster the web documents based on the similarity of underlying template structures in

the documents so that the template for each cluster is extracted simultaneously. We develop a novel goodness

measure with its fast approximation for clustering and provide comprehensive analysis of our algorithm. Our

experimental results with real-life data sets confirm the effectiveness and robustness of our algorithm compared to

the state of the art for template detection algorithms.












73 The CoQUOS Approach to Continuous Queries in Unstructured Overlays

The current peer-to-peer (P2P) content distribution systems are constricted by their simple on-demand content

discovery mechanism. The utility of these systems can be greatly enhanced by incorporating two capabilities,

namely a mechanism through which peers can register their long term interests with the network so that they can be

continuously notified of new data items, and a means for the peers to advertise their contents. Although researchers

have proposed a few unstructured overlay-based publishsubscribe systems that provide the above capabilities, most

of these systems require intricate indexing and routing schemes, which not only make them highly complex but also

render the overlay network less flexible toward transient peers. This paper argues that for many P2P applications,

implementing full-fledged publish-subscribe systems is an overkill. For these applications, we study the alternate

continuous query paradigm, which is a best-effort service providing the above two capabilities. We present a

scalable and effective middleware, called CoQUOS, for supporting continuous queries in unstructured overlay

networks. Besides being independent of the overlay topology, CoQUOS preserves the simplicity and flexibility of the

unstructured P2P network. Our design of the CoQUOS system is characterized by two novel techniques, namely

cluster-resilient random walk algorithm for propagating the queries to various regions of the network and dynamic

probability-based query registration scheme to ensure that the registrations are well distributed in the overlay.

Further, we also develop effective and efficient schemes for providing resilience to the churn of the P2P network and

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for ensuring a fair distribution of the notification load among the peers.

74 The World in a Nutshell: Concise Range Queries

With the advance of wireless communication technology, it is quite common for people to view maps or get related services

from the handheld devices, such as mobile phones and PDAs. Range queries, as one of the most commonly used tools, are

often posed by the users to retrieve needful information from a spatial database. However, due to the limits of

communication bandwidth and hardware power of handheld devices, displaying all the results of a range query on a

handheld device is neither communicationefficient nor informative to the users. This is simply because that there are often

too many results returned from a range query. In view of this problem, we present a novel idea that a concise

representation of a specified size for the range query results, while incurring minimal information loss, shall be computed

and returned to the user. Such a concise range query not only reduces communication costs, but also offers better

usability to the users, providing an opportunity for interactive exploration. The usefulness of the concise range queries is

confirmed by comparing it with other possible alternatives, such as sampling and clustering. Unfortunately, we prove that

finding the optimal representation with minimum information loss is an NP-hard problem. Therefore, we propose several

effective and nontrivial algorithms to find a good approximate result. Extensive experiments on real-world data have

demonstrated the effectiveness and efficiency of the proposed techniques.

75 Usher: Improving Data Quality with Dynamic Forms

Data quality is a critical problem in modern databases. data-entry forms present the first and arguably best

opportunity for detecting and mitigating errors, but there has been little research into automatic methods for

improving data quality at entry time. In this paper, we propose USHER, an end-to-end system for form design, entry,

and data quality assurance. Using previous form submissions, USHER learns a probabilistic model over the

questions of the form. USHER then applies this model at every step of the data-entry process to improve data quality.

Before entry, it induces a form layout that captures the most important data values of a form instance as quickly as

possible and reduces the complexity of error-prone questions. During entry, it dynamically adapts the form to the

values being entered by providing real-time interface feedback, reasking questions with dubious responses, and

simplifying questions by reformulating them. After entry, it revisits question responses that it deems likely to have

been entered incorrectly by reasking the question or a reformulation thereof. We evaluate these components of

USHER using two real-world data sets. Our results demonstrate that USHER can improve data quality considerably at

a reduced cost when compared to current practice.

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