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 University

Amit BaggaAvaya Labs Research

Presenter: Amit Bagga

2Copyright© 2003 Avaya Inc. All rights reserved

Outline of the presentation

• Motivations and goals

• Application overview

• Functionalities

• Architecture

• Algorithms

• Implementation features

• Demo


Motivations and Goals

• Motivations– Current data mining techniques do not look at unstructured

text information in large database.

– NSM and service engineers currently manually scan text fields to identify, track and classify problems across customers, locations and products.

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

NSM and service engineers.

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


Overview: what we develop

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

– Find similar problems

– Identify sub problems

– Trace similar problems of certain customer and product


Overview: data source

• Maestro database

– Huge amount of information

– High dynamics

• The database maintained by Patrick Tendick

– A subset of Maestro database

– 1 million records, 600 thousand tickets

– TOOS (Totally Out Of Service) cases: high severity

• Unstructured data as pure text

– Ticket description

– Resolution description.

• Structured data in relational database

– Case ID, timestamp, product, customer, location, etc.


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

– Help to 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 of certain or different 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


An 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

……


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

Structured data fields

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: 10,708 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

• The TFIDF modular: two different versions.


Algorithm: hierarchical clustering

• Clustering– A data mining technique to find data

aggregates in multi-dimensional space.

– Data representation

• Each data item has many different attributes

• Each attribute is a dimension in vector space.

• Each data item is a vector whose elements are values of attributes.


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

• 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


Demo

• Web entry– http://amit-pc.woods.avayalabs.com/xui/xui-web/xui.shtm


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

• Scalability

• Adaptive search– Users’ feedback

Copyright© 2003 Avaya Inc. All rights reserved Avaya Interactive Dashboard (AID): An Interactive...

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