WebKDD 2004 Workshop on Web Mining and Web Usage Analysis Held in conjunction with The 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004) August 22-25, 2004, Seattle, Washington

Workshop Description

The Web represents a key driving force for a large spectrum of applications in which users interact with or within companies, organizations, governmental agencies, and educational or collaborative environments. User preferences and expectations, together with usage, content, and structural patterns obtained from the Web, form the basis for intelligent, personalized, and business-optimal services. Key Web business metrics enabled by proper data collection and processing are essential to run an effective business or service. Enabling technologies include data mining, scalable data warehousing and preprocessing, sequence discovery, real time processing, document classification, user modeling and evaluation models. Recipient technologies that demand for user profiling and usage patterns include recommendation systems, Web analytics applications, content management systems, and fraud or intrusion detection systems. The inherent and increasing heterogeneity of the Web has required Web-based applications to more effectively integrate a variety of types of data across multiple channels and from different sources. The development of techniques and architectures for more effective integration and mining of content, usage, and structure data from different sources is likely to lead to the next generation of more useful and more intelligent Web applications. The WebKDD'04 workshop aims to bring together practitioners and researchers from both academia and industry in order to foster the exchange of ideas and the dissemination of emerging solutions for intelligent Web-based applications using Web usage, structure and content mining.

Workshop Topics

We invite submissions covering the full range of topics related to Web mining and Web usage analysis, from foundational issues of obtaining, modeling, integrating, and mining of Web usage, content, and structure data, to the deployment and evaluation of Web mining techniques in concrete architectures and systems. We especially encourage methodological contributions covering new developments in the basic enabling technologies required for intelligent Web applications and services. Topics of interest include: Web Usage Mining and Web Analytics Web usage preprocessing Novel techniques for discovery and analysis of Web usage patterns Integrating semantics and domain knowledge in Web usage mining and analysis Reliability and consistency of Web metrics Integration of click stream data with back-end data and related metrics Intelligent summarization/explanation of changes in Web usage metrics Web Content and Structure mining Integration of Web content, usage, and structure data for Web mining Text mining techniques for generating meta-data Classification and clustering of text and multimedia content Detecting emerging trends or topics in text Adaptive content management Discovery and analysis of online communities and referral networks Enabling Technologies Data warehousing (both Web and non-Web data) Data collection and preparation Data visualization techniques Representing, modeling, and reasoning with Web usage patterns Knowledge Management in Web mining Multi-relational data mining applied to Web mining User Modeling and Profiling Generating and updating profiles from implicit or explicit user preferences Discovering misuse and fraud through outlier analysis Personalized taxonomies or ontologies for navigation assistance Cognitive models for Web navigation and e-commerce interactions Incremental user modeling in dynamic environments Permission marketing Applications Recommendation and personalization systems Intelligent Web services Contextual information access and retrieval Alert and Information filtering systems Adaptive hypertext systems Fraud and misuse detection, such as credit-card fraud and network intrusion detection Web mining applications for business and competitive intelligence Log analysis for security applications

Paper Submission

All submissions must be made electronically to the workshop contact, Bamshad Mobasher, at mobasher @ cs . depaul . edu. All papers must be original, and have not been published elsewhere. The workshop proceedings will be published by the ACM and distributed during the workshop. The full version of the accepted papers will be published by Springer-Verlag after a second round of reviews pending approval. Note: Please also check the list of topics in the KDD Workshop on Mining for and from the Semantic Web (MSW04). You should consider submitting your paper to MSW04, if the primary topics covered by the paper relate more directly to the Semantic Web, ontologies, and related technologies. Given certain amount of overlap between the topics covered by the two workshops, some of the papers submitted to each workshop will be selected for presentation at a joint session. More details about the joint session will be posted at a later date.

Important Dates

June 9, 2004: Electronic submission of full papers July 1, 2004: Author notification July 10, 2004: Submission of Camera-ready papers (hard deadline) August 22, 2004: Workshop in Seattle, Washington

Workshop Co-Chairs

Note: for inquiries please send e-mail to mobasher @ cs . depaul . edu. Bamshad Mobasher, DePaul University, Chicago. Bing Liu, University of Illinois at Chicago. Brij Masand, Data Miners, Inc. Olfa Nasraoui, University of Louisville.