Web Log Session Analyzer Integrating Parsing and Logic Programming into a Data Mart Architecture

• Published in: IEEE/WIC/ACM International Conference on web intelligence pp. 794 - 797
• Main Author: Desmarais, Michel C.
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 19.09.2005; IEEE
• Series: ACM Conferences
• Subjects: Application software; Applied computing > Document management and text processing; Computer architecture; Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Logic programming and answer set programming; Computing methodologies > Artificial intelligence > Natural language processing; Data engineering; Electric breakdown; Hair; Information systems > Information retrieval > Users and interactive retrieval > Personalization; Information systems > World Wide Web > Web searching and information discovery > Personalization; Logic programming; Navigation; Service oriented architecture; Skin; Software and its engineering > Software notations and tools > Context specific languages > Domain specific languages; Software and its engineering > Software organization and properties > Software system structures > Software architectures; Theory of computation > Logic > Constraint and logic programming
• ISBN: 076952415X; 9780769524153
• DOI: 10.1109/WI.2005.159

Navigation and interaction patterns of Web users can be relatively complex, especially for sites with interactive applications that support user sessions and profiles. We describe such a case for an interactive virtual garment dressing room. The application is distributed over many web sites, supports personnalization and user profiles, and the notion of a multi-site user session. It has its own data logging system that generates approximately 5GB of complex data per month. The analysis of those logs requires more sophisticated processing than is typically done using a relational language. Even the use of procedural languages and DBMS can prove tedious and inefficient. We show an approach to the analysis of complex log data based on a parallel stream processing architecture and the use of specialized languages, namely a grammatical parser and a logic programming module, that offers an efficient, flexible, and powerful solution.