TY - BOOK ID - 12249201 TI - WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles : 4th International Workshop, Edmonton, Canada, July 23, 2002, Revised Papers AU - Zaiane, Osmar R. AU - Srivastava, Jaideep. AU - Spiliopoulou, Myra. AU - Masand, Brij. AU - WEBKDD 2002 PY - 2003 SN - 3540396632 3540203044 PB - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, DB - UniCat KW - Web usage mining KW - Internet users KW - Library & Information Science KW - Social Sciences KW - Web users KW - World Wide Web users KW - Analysis, Web usage KW - Analytics, Web KW - Mining, Web usage KW - Web analytics KW - Web usage analysis KW - Popular works. KW - Computer communication systems. KW - Database management. KW - Information storage and retrieval. KW - Artificial intelligence. KW - Computer science. KW - Popular Science. KW - Popular Computer Science. KW - Artificial Intelligence (incl. Robotics). KW - Computer Communication Networks. KW - Database Management. KW - Information Storage and Retrieval. KW - Information Systems Applications (incl. Internet). KW - Informatics KW - Science KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Data base management KW - Data services (Database management) KW - Database management services KW - DBMS (Computer science) KW - Generalized data management systems KW - Services, Database management KW - Systems, Database management KW - Systems, Generalized database management KW - Communication systems, Computer KW - Computer communication systems KW - Data networks, Computer KW - ECNs (Electronic communication networks) KW - Electronic communication networks KW - Networks, Computer KW - Teleprocessing networks KW - Data transmission systems KW - Digital communications KW - Electronic systems KW - Information networks KW - Telecommunication KW - Cyberinfrastructure KW - Network computers KW - Distributed processing KW - Computer users KW - Personal Internet use in the workplace KW - Data mining KW - Information storage and retrieva. KW - Artificial Intelligence. KW - Information storage and retrieval systems. KW - Automatic data storage KW - Automatic information retrieval KW - Automation in documentation KW - Computer-based information systems KW - Data processing systems KW - Data storage and retrieval systems KW - Discovery systems, Information KW - Information discovery systems KW - Information processing systems KW - Information retrieval systems KW - Machine data storage and retrieval KW - Mechanized information storage and retrieval systems KW - Computer systems KW - Electronic information resources KW - Data libraries KW - Digital libraries KW - Information organization KW - Information retrieval KW - Application software. KW - Application computer programs KW - Application computer software KW - Applications software KW - Apps (Computer software) KW - Computer software UR - https://www.unicat.be/uniCat?func=search&query=sysid:12249201 AB - 1 WorkshopTheme Data mining as a discipline aims to relate the analysis of large amounts of user data to shed light on key business questions. Web usage mining in particular, a relatively young discipline, investigates methodologies and techniques that - dress the unique challenges of discovering insights from Web usage data, aiming toevaluateWebusability,understandtheinterestsandexpectationsofusersand assess the e?ectiveness of content delivery. The maturing and expanding Web presents a key driving force in the rapid growth of electronic commerce and a new channel for content providers. Customized o?ers and content, made possible by discovered knowledge about the customer, are fundamental for the establi- ment of viable e-commerce solutions and sustained and e?ective content delivery in noncommercial domains. Rich Web logs provide companies with data about their online visitors and prospective customers, allowing microsegmentation and personalized interactions. While Web mining as a domain is several years old, the challenges that characterize data analysis in this area continue to be formidable. Though p- processing data routinely takes up a major part of the e?ort in data mining, Web usage data presents further challenges based on the di?culties of assigning data streams to unique users and tracking them over time. New innovations are required to reliably reconstruct sessions, to ascertain similarity and di?erences between sessions, and to be able to segment online users into relevant groups. ER -