Currently a plethora of heterogeneous, standalone or web-enabled applications exist providing various functionalities that could be exploited in innumerable contexts for the developmentof personalized agent-basedsolutions for the end user. The integration of these applications in a standard and seamless way to enable content-rich services for the end-user is not generally feasible. The main reason for this is that these app- cations are by nature heterogeneous, developed for different development plat-forms, using different software developmenttechnologies. In this paper we present a reference architecture and support tools designed to address the problem of seamless integration ofheterogeneoussoftwareapplicationsthroughdata mining(DM) onweb service(WS) data ("web service mining") in order to enhance personalization, pervasiveness and - ?ciency on behalf of agent-based end-user applications. 1 Work presented in this paper is part of a European funded project called OASIS, whose main objective is the implementation of an ontology-driven, open reference - chitecture, which will enable and facilitate interoperability, seamless connectivity and sharing of content between different services and ontologiesin application domains for the elderly and beyond. OASIS promotes new ways to integrate all supported appli- tions into a common environment that enables access to information and content from the existing applications through WS-based software interfaces and content delivery to end-user in a pervasive manner through multi-agent applications. The achievement of this objectiveforms the main motivationof our work that results in a WS mining fra- work for the delivery of personalizedservices to the elderly users througha multi-agent system (MAS).

InhaltsangabeAgents for Data Mining.- Finding Useful Items and Links in Social and Agent Networks.- Integrating Workflow into Agent-Based Distributed Data Mining Systems.- Pilot Study: Agent-Based Exploration of Complex Data in a Hospital Environment.- Multi-agent Information Retrieval in Heterogeneous Industrial Automation Environments.- Data Mining for Agents.- A Data Mining Approach to Identify Obligation Norms in Agent Societies.- Probabilistic Modeling of Mobile Agents' Trajectories.- Real-Time Sensory Pattern Mining for Autonomous Agents.- Data Mining in Agents.- Analyzing Agent-Based Simulations of Inter-organizational Networks.- Clustering in a Multi-Agent Data Mining Environment.- Time-Based Reward Shaping in Real-Time Strategy Games.- Wise Search Engine Based on LSI.- Pattern Recognition in Online Environment by Data Mining Approach.- Agent Mining Applications.- A Multiple System Performance Monitoring Model for Web Services.- Implementing an Open Reference Architecture Based on Web Service Mining for the Integration of Distributed Applications and Multi-Agent Systems.- Minority Game Data Mining for Stock Market Predictions.