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 Ask Jeeves, Inc. USA Ziyang Wang Department of Computer Science New York University USA

2. Outline • Motivations and goals • Application overview • Functionalities • Architecture • Algorithms • Implementation features • Future directions

3. 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

4. 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.

5. 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

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 • Helps 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 by 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. 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

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

12. Architecture: Text Analysis Module 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

13. 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).

14. 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

15. 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

16. 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.

17. 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

18. 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 dpo tech music csr overhead cust check report Example

19. 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

20. 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

21. 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

22. 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