learning to create data-integration queries partha pratim talukdar, marie jacob, muhammad salman...

Learning to Create Data-Learning to Create Data-Integration QueriesIntegration Queries

Partha Pratim Talukdar, Marie Jacob, Muhammad Salman Mehmood, Koby Partha Pratim Talukdar, Marie Jacob, Muhammad Salman Mehmood, Koby Crammer, Zachary G. Ives, Fernando Pereira, Sudipto GuhaCrammer, Zachary G. Ives, Fernando Pereira, Sudipto Guha

VLDB2008 VLDB2008

Seminar Presented bySeminar Presented byNoel GunasekarNoel Gunasekar

CSE Department – SUNY BuffaloCSE Department – SUNY Buffalo

Learning to Create Data Integration Queries2

Learning to Create Data-Integration QueriesIntroduction

Motivation Example

Existing solutionsQ-System Solution

Q-System ArchitectureQuery and Query AnswersExecuting QueryLearning From feedback

ConclusionExperimental resultsFuture Work


Introduction


Motivation

Need for non-expert user to pose queries across multiple data resources.

• Non-expert user - Not familiar with querying languages

• Multiple resource - Databases, Data warehouses, Virtual integrated schemas

Bio-Science Field

Learning to Create Data Integration Queries 5

• Many "standardized" databases with overlapping and cross-referenced information

• Each site is being independently extended, corrected, and analyzed

• Differing levels of data quality/confidence

Protein Databases

Protein DataBase: - PDB information and service listings at Brookhaven National Laboratory [ BNL ]PIR: - Protein Identification Resource database at [ JHU ] PRF: - Protein Research Foundation database at GenomeNet SwissProt - Protein database at ExPASy [ Switzerland ]

Example


genomics

Life Sciences

Researcher Disease Studies

Life Sciences researcher querying on data-sources like genomics, disease studies and pharmacology. Pharmacolog

y

“What are the proteins and genes associated with the disease Narcolepsy?”

http://www.expasy.org/uniprot/P04049


Existing Solution

Using keyword based queries on Web-Forms

Match the keywords with terms in the tuples and form the query by joining different databases using foreign-keys

• Cost for the query is fixed and doesn’t accommodate the context of the query

http://www.expasy.org/uniprot/P0C852

http://www.expasy.org/uniprot/P04049


Proposed Solution - Q System

Automatically generate Web-Forms for given set of keywords

Pose queries across multiple data resources using the generated web-form


Proposed Solution - Q System

Q SystemKeywords

Protein, gene, disease Reusable Web-Form For querying

User(Author)

Create re-usable web-form

Use web-form for Querying

Reusable Web-Form For querying

ParametersUsers

(Author + others)Query Results


Q System Architecture


Architecture of Q System

Four Components

•Initial Schema Loader

•Query Template Creation

•Query Execution

•Learning Through Feedback


Architecture of Q System


Initial Setup

Schema Loader

Input

• Given a set of data sources with its own schema• Foreign Keys and Links• Schema Mappings• Record Link

Output

• Schema Graph


Example Schema Graph

Node: Databases and their attributes (UniProt database, Entrez GeneInfo db, term)Edge: Relation based on foreign keys/cross-references (UniProt to PIR)Cost: Reliability, completeness

cb

d

0.070.1 0.04

Initial Setup


Query Template Creation


Query Template Creation - Example

Input: “protein”, “plasma membrane”, “gene” and “disease”

Output:

Find trees connecting red

nodes

e

a cb

fd

0.070.1

0.1

0.10.04

0.1

Schema Graph

Rank = 2

Cost = 0.41

Rank = 1

Cost = 0.4e

a cb

fd

0.07

0.1

0.10.04

0.1

e

a b

fd

0.1

0.1

0.1

0.1

Query Keywords a, e, f

Q2Q1

Query Template Creation - Example

Query Formulation

•Trees can be easily written as executable queries:

Steiner Tree

Conjunctive query: a(x,y),b(y,z),d(z,w),e(w,u),f(w,v)

e

a b

fd

0.1

0.1

0.1

0.1

View Refinement

Web-Form


Query Execution

Input: Web-Form

Output: Result Answers

Q1

Q1

Q1,2

Q2

Q2Q2

System determines “producer” queries using provenance


Query Execution

Query Processing Engine with

• Support for querying remote data sources• Record data provenance

Solution:

ORCHESTRA

http://www.cis.upenn.edu/~zives/orchestra/


Orchestra Project

• The ORCHESTRA project focuses on the challenges of data sharing scenarios in the sciences

• Bioinformatics Scenario - many "standardized" databases with overlapping information, similar but not identical data and differing levels of data quality/confidence

• Each site is being independently extended, corrected, and analyzed

•ORCHESTRA collaborative data sharing system (CDSS) is on how to support reconciliation across different schemas, with disagreeing users


Orchestra Project – Data Provenance

http://www.cis.upenn.edu/~zives/research/exchange.pdf


Learning through Feedback


• Input: Ranked Results + provenance

Q1

Q1

Q1,2

Q2

Q2Q2



• User provides feedback

Q1

Q1

Q1,2

Q2

Q2Q2


Query Formulation - Recap

Find trees connecting red

nodes

e

a cb

fd

0.070.1

0.1

0.10.04

0.1

Schema Graph

Rank = 2

Cost = 0.41

Rank = 1

Cost = 0.4e

a cb

fd

0.07

0.1

0.10.04

0.1

e

a b

fd

0.1

0.1

0.1

0.1

Query Keywords a, e, f

Q2Q1

e

a cb

fd

0.1

0.1

0.1 0.04

0.1

e

a cb

fd0.1

0.1 0.04

0.1

e

a b

fd

0.1

0.1

0.1

0.1

Change weights so Q2 is “cheaper” than Q1

Rank = 1

Cost = 0.4

Rank = 2

Cost = 0. 41

Rank = 2

Cost = 0.4

Rank = 1

Cost = 0.39

0.05

Q1 Q20.05

0.07

0.07



Iteration!

Q-System: Challenges

Computation of ranked queries which in turn produce ranked tuples: K-Best Steiner Tree Generation

Predicting new query rankings based on user feedback over tuples, and also generalizing feedback: Learning

Maintaining associations between tuples and queries: Query answers with provenance

Everything at interactive speed!

Cost of a Query

•Query Cost = Sum of edge costs in the tree.•Edge Cost = Sum of weights of features defined over it.

•Features are properties of the edges, e.g., nodes connected•Each feature has a corresponding weight. •Feature example:

Term Synonymf 1 w8

f = 1 if the edge connects Term and Synonym tables, else 0

Steiner Trees: Finding Lowest-Cost Queries A tree of minimal cost in a graph (G)

which includes all the required nodes (S).

Cost of a Steiner Tree is the sum of costs of edges present in the tree.

Steiner Tree is generalization of Minimum Spanning Tree (MST) [equivalent when S = all vertices in G].

e

a cb

fd

0.070.1

0.1

0.10.04

0.1

K-Best Steiner Tree Algorithms

• Exact (practical for ~100 nodes and edges).• Integer Linear Program (ILP) based formulation

for finding K-best Steiner Trees in a graph. • The ILP uses ideas from multi-commodity network

flows

• Approximate (for 100s+ nodes and edges).• Novel Shortest Paths Complete Subgraph

Heuristic.• Significantly faster; in practice, often gives optimal

solution.

Multi-Commodity Flow Problem

MIP for min-cost Steiner Tree

MIP for K min-cost Steiner Tree

Constraints

C1 : Flow of commodity k starts at root rC2 : Flow of commodity k terminates at node kC3 : Conservation of flow at Steiner nodesC4 : Flow of an edge allowed only if that edge is

included ( Yij = 1 )C5 : Non-negativity constraintC6 : Defines value for YC7 : Ensures no incoming active edge into the rootC8 : Ensures that all nodes have at most one incoming

active edgeC9 : Flow of at least one commodity on all edges in IC10 : Ensures no flow on edges in X

Finding K-Best Steiner Trees

2-best Steiner trees connecting terminal nodes.

e

a cb

fd

0.070.1

0.1

0.10.04

0.1

Rank = 2

Cost = 0.41

Rank = 1

Cost = 0.4e

a cb

fd

0.07

0.1

0.10.04

0.1

e

a b

fd

0.1

0.1

0.1

0.1

K-Best Steiner Tree Algorithms

• Approximate (for 100s+ nodes and edges).• Novel Shortest Paths Complete Subgraph

Heuristic.• Send “m” shortest path graph as input.• Shortest path between each pair of nodes in S• Significantly faster; in practice, often gives optimal

solution.

Q Challenge : Getting User Feedback

e

a b

fd

T

e

a b

fd

c

T*

Q

Q*

.

.

.

Query

.

.

.

Tuples

.

.

.

Top

Bottom

T and T* differ in 3 edges. This difference is termed loss: L(T, T*)

Learning: Update Weights

Term2TermTerm(T1)Edge Cost: 0.07

w8 = 0.06w25 = 0.01

Term2TermTerm(T1)Edge Cost: 0.05

w8 = 0.04w25 = 0.01

Edge Cost Update

Re-ranked Steiner Trees

Weight Update

Rank 1 Rank 2

e

a cb

fd

0.07

0.1

0.10.04

0.1

e

a b

fd

0.1

0.1

0.1

0.1

e

a cb

fd

0.05

0.1

0.10.04

0.1

e

a b

fd

0.1

0.1

0.1

0.1

Experimental Results

The Key Questions• Can the algorithm start with uninitialized weights and learn

expert (“gold standard”) ranking of queries?

• Can the results be generated at interactive speeds?

• Does the approach scale to larger graphs?

Results: Learning Expert Weights

•Graph: Start with the BioGuide bio sources, with 28 vertices and 96 edges.

•Goal: Learn the queries corresponding to the expert-set weights in BioGuide

• Methodology:• All weights are set to default. • Sequence of 25 searches•For each, user feedback identifies & promotes a tuple from the gold standard answer.

• After 40-60% searches with feedback, system finds the top query immediately.• For each individual search, a single feedback is enough to learn the top query.

# G

old

quer

ies

abse

nt in

top

-3 p

redi

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ns

Results: Time to generate K-best Queries

K Time (s)

1 0.115 2.0010 4.0220 8.42

• Schema graph of size (28, 96) from BioGuide (Boulakia et al., 2007).

It is possible to generate the top query in < 1 sec and top 5 queriesin about 2 sec, all within interactive range. Query execution is pipelined.

Results: Scalability to Larger Graphs

K Speedup

Error

1 12 02 14.6 03 20.3 05 72.4 0

•Larger schema graph of size (408, 1366) from real sources: GUS, GO, BioSQL.

It is possible to do K-best inference in larger graphs quickly and with little or no loss (none

in this case).

Queries (K)


Experimental Results

“Gold Standard”

• Using BioGuide – a biomedical information integration system

• BioGuide generates the schema graph based on keywords

• The edge cost in the schema graph are manually assigned by experts

• This expert given schema graph is called the “gold standard”

Experiment involves in comparing the result produced by the q system with the results produced by the gold standard.


Learning against expert cost

• Started with an expert query template

“What are the related proteins in [DB1] and genes in [DB2] associated with disease Narcolepsy in [DB3] ?

• By instantiating the template 25 queries were formed

• Each time for a query the lowest-cost Steiner tree is computed

• The “gold standard” is used as the feedback and learning is done


Future Work

• Work on other approximation algorithms for computing the Steiner trees

• Evaluation against real biological applications

• Incorporating data-level keyword matches.

learning to create data-integration queries partha pratim talukdar, marie jacob, muhammad salman...

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