copyright© 2003 avaya inc. all rights reserved avaya interactive dashboard (aid): an interactive...

Copyright© 2003 Avaya Inc. All rights reserved

Avaya Interactive Dashboard (AID): An Interactive Tool for Mining Avaya Problem

Ticket Database

Ziyang WangDepartment of Computer Science

New York UniversityUSA

Amit BaggaAsk Jeeves, Inc.

USA

2Copyright© 2003 Avaya Inc. All rights reserved

Outline

• Motivations and goals

• Application overview

• Functionalities

• Architecture

• Algorithms

• Implementation features

• Future directions


Avaya Problem Database (Maestro)

• Approximately 5 million records– Appx. 4 million alarms (reported via self-diagnostic tool)

– Appx. 1 million tickets (reported by customer via a phone call)

• Ticket records– Structured data fields

• Customer name, location, product, date of problem, etc.

– Unstructured fields (each limited to length of 256 bytes)• Problem description

• One or more notes fields

• Resolution description


Motivations and Goals

• Motivations– Structured fields mined by traditional data mining algorithms

– NSM and service engineers manually scanned text fields • First restricted size with a database query (based upon structured

fields)

• Manually scanned resulting set of records to identify, track and classify problems across customers, locations and products.

• Goals– Develop interface that helps automate text analysis done by

NSM and service engineers.

– Provide advanced functionality to help them quickly and conveniently discover patterns and verify intuitions about problems.


Application Overview

• Interactive Dashboard– A tool using search and data mining techniques

– Find similar problems

– Identify sub problems

– Trace similar problems by customer and product


Overview: algorithms and implementation

• Interactive Dashboard

– Programming languages: Java, Perl, C

– Service model: sockets, client/server model

– Database management: Oracle, JDBC

– Relevance metric: TF*IDF

– Clustering: hierarchical clustering

– Web interface: Perl, CGI


Functionalities: Major ones

• Search relevant tickets– Help to find similar problems

– Relevance score: the similarity of unstructured text data.

– Search constrains: product name, customer code, time and severity of tickets.

– Top level summary: ticket case ID, relevance score, ticket description.

• Cluster relevant tickets– Group similar tickets into clusters

– Helps identify sub problems

– Keyword expansion

– Adaptive online search


Functionalities: Supporting ones

• Categorize a set of tickets– Categorized by product name, customer name, and location name

– Provide a high level summary

– Discover similar problems by customers, products

• Retrieve detailed ticket information– Complete product/customer/location information, ticket resolution

note, etc.


Functionalities (cont.)

• Accessibility

Web portal

Relevant TicketsCategorized

Set

ClusteredRelevant Tickets

TicketInformation


Interactive Dashboard

Architecture: Main Frame

• Main frame: application server infrastructure

– 3-tier server architecture

– Integrated central server: service provider and server logic organizer

DatabaseWeb

InterfaceIntegrated

Central ServerCGI JDBC


Architecture: Integrated Central Server

Integrated Central Server

Server Socket Module

Query Engine

Database module

Text Analysis Module

Response Module

Incoming requests

Output results

Database


Architecture: Text Analysis Module

Text Analysis Module

Database module Database

Stop wordsText Filter

Data module Functional module

Clustering

Dictionary

Relevance Evaluator

Keywords/Sample

Unstructured data

TFIDF Module

TopRelevantTickets

Response Module

Output Manager Document Frequency

Categorizing


Algorithm: TFIDF

• TFIDF: a similarity metric for text data

– Text document view: a bag of words.

– Document representation: a vector .

– The similarity of two documents is the normalized inner product of two vectors (the cosine of two vector).

,....),( 21 iii wwD

kikkikik DF

NTFIDFTFw 0log)()()(

22jkik

jkik

ji

iiij

ww

ww

DD

DDSimilarity


Algorithm: TFIDF (cont.)

• Issues– Document frequency

• Global vs. local

• Vocabulary: 100,708 terms after text filtering

• Solution: offline scan of database

– Term frequency

• Online scan of ticket description

• Text filtering

– Computing the similarity of ticket description

• Searching relevant tickets: 1-to-N similarity

• Clustering: N-to-N similarity


Algorithm: Hierarchical Clustering (cont.)

• Hierarchical clustering– Similarity metric of data vector: TFIDF, Euclidean

– Hierarchical clustering

• Step-by-step bottom-up cluster merging

• Merging criteria: complete linkage

• Cost: N-square performance


Implementation Features

• Integrated server is built like a web-server where backend is a database

– Multi-threaded-model

– Stateless

• High level SQL query processor– Maps multiple requests to single database connection

• Loading database driver and authentication are done only once.

• Reducing the slow start of database connection.

• Using multiple JDBC SQL statements over one database connection can schedule data transmission “looks like” parallel retrieval.


Example

PLAT Csr cld to report trouble on Paging System, TOOS. No overhead music, no MOH. DPO tech SEV 4 dispatch to diagnose.

PLAT Csr cld to report trouble on Paging System, TOOS. No overhead music, no MOH. DPO tech SEV 4 dispatch to diagnose.

PLAT Csr cld to report paging system TOOS, no overhead music at all. System has been reset by csr (power confirmed). DPO tech SEV 4 to diagnose.

PLAT Csr cld to report trouble with overhead music, TOOS. Paging appears OK, but they cannot get music output. DPO tech SEV 4 dispatch to check volume levels.

……

paging plat dpotech music csroverhead custcheck report

tech dpo platcsr access assist

speakers overheaddiagnose x15255

paging plat dpo tech sev power csr diagnose

overhead carrier

……


Example

PLAT Csr cld to report paging system TOOS, no overhead music at all. System has been reset by csr (power confirmed). DPO tech SEV 4 to diagnose.

……

paging plat dpotech music csroverhead custcheck report

Ticket # Customer

08072003.131 ABC Corp.

08152003.045 ABC Corp.

09032003.083 ABC Corp.


Evaluation

• Hard since there are 1 million tickets in database

• Based upon detailed feedback from 2 of the users:

– Significantly improves productivity

– Additional features identified that are in process of being implemented

• Example: automatic identification of root cause, prediction of resolution code based upon prior cases of similar problems


Future directions

• Search precision– Refine algorithms of relevance computation

– Refine algorithms of clustering

– Text filtering

• Search performance– Database organization

– Java primitive functions

• Automatic classification of root cause of problems– Machine learning approach

• Prediction of resolution code

• Scalability


Implementation: features

• Abstract database SQL manager for parallel requests– Mapping parallel requests to single database connection:

• Loading database driver and authentication are done only once.

• Reducing the slow start of database connection.

• Using multiple JDBC SQL statements over one database connection can schedule data transmission “looks like” parallel retrieval.

– Stateful abstract database connection manager

• Unified error message processor– Exception catching and re-throwning

– Goodness

• Format error message as HTML text

• Secure database connection status to be consistent


Implementation: features (cont.)

• Multiple system-dependent process interaction through java runtime

– Kernel clustering modular is written in C

• High performance for numerical computation

• Unix/Linux OS required

– Communication of processes

– I/O redirection

• Extensibility– Search space

– Localized index engine

copyright© 2003 avaya inc. all rights reserved avaya interactive dashboard (aid): an interactive...

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product slide

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