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Applications like multimedia databases or enterprisewide information management systems have to meet the challenge of efficiently retrieving best matching objects from vast collections of data. We present a new algorithm Stream-Combine for processing multi-feature queries on heterogeneous data sources. Stream-Combine is seljadapting to different data distributions and to the specific kind of the combining function. Furthermore we present a new retrieval strategy that will essentially speed up the output of relevant objects.

Abstract.Advanced personalized e-applications require comprehensive knowledge about their user's likes and dislikes in order to provide individual product recommendations, personal customer advice and custom-tailored product offers. In our approach we model such preferences as strict partial orders with "A is better than B" semantics, which has been proven to be very suitable in various e-applications. In this paper we present novel Preference Mining techniques for detecting strict partial order preferences in user log data. The main advantage of our approach is the semantic expressiveness of the Preference Mining results. Experimental evaluations prove the effectiveness and efficiency of our algorithms. Since the Preference Mining implementation uses sophisticated SQL statements to execute all data-intensive operations on database layer, our algorithms scale well even for large log data sets. With our approach personalized e-applications can gain valuable knowledge about their customers' preferences, which is essential for a qualified customer service.

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