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

1. Avaya Interactive Dashboard (AID): An Interactive Tool for Mining Avaya Problem Ticket Database Amit Bagga Avaya Labs Research Presenter: Amit Bagga Ziyang Wang Department of Computer Science New York University

2. Outline of the presentation • Motivations and goals • Application overview • Functionalities • Architecture • Algorithms • Implementation features • Demo

3. 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.

4. 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

5. 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.

6. 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

7. 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

8. 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.

9. Functionalities (cont.) • Accessibility Web portal Categorized Set Relevant Tickets Ticket Information Clustered Relevant Tickets

10. 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. …… …… tech dpo plat csr access assist speakers overhead diagnose x15255 paging plat dpo tech sev power csr diagnose overhead carrier paging plat dpo tech music csr overhead cust check report

11. Architecture: Main Frame • Main frame: application server infrastructure • 3-tier server architecture • Integrated central server: service provider and server logic organizer Interactive Dashboard Integrated Central Server Database Web Interface JDBC CGI

12. Architecture: Integrated Central Server Integrated Central Server Incoming requests Server Socket Module Query Engine Database Database module Text Analysis Module Output results Response Module

13. Architecture: Text Analysis Module Structured data fields Database module Database Unstructured data Text Analysis Module Keywords/Sample Stop words Text Filter Dictionary Relevance Evaluator TFIDF Module Top Relevant Tickets Document Frequency Output Manager Categorizing Clustering Response Module Functional module Data module

14. 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).

15. 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.

16. 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.

17. 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

18. 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

19. 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

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

21. 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

22. Acknowledgements Patrick Tendick Ping Zhang Joann OrdilleHamilton SlyeAnd other members in Avaya XUI group