Werner Kießling

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Personalization of e-services poses new challenges to database technology, demanding a powerful and flexible modeling technique for complex preferences. Preference queries have to be answered cooperatively by treating preferences as soft constraints, attempting a best possible match-making. We propose a strict partial order semantics for preferences, which(More)
In digital libraries image retrieval queries can be based on the similarity of objects, using several feature attributes like shape, texture, color or text. Such multi-feature queries return a ranked result set instead of exact matches. Besides, the user wants to see only the k top-ranked objects. We present a new algorithm called Quick-Combine (European(More)
Personalization of database queries requires a semantically rich, easy to handle and flexible preference model. Building on preferences as strict partial orders we provide a variety of intuitive base preference constructors for numerical and categorical data, including so-called d-parameters. As a novel semantic concept for complex preferences we introduce(More)
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(More)
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 selfadapting to different data(More)
Preference SQL is a declarative extension of standard SQL by strict partial order preferences, behaving like soft constraints under the BMO query model. Preference queries can be formulated intuitively following an inductive constructor-based approach. Both qualitative methods like e.g. Pareto / skyline and quantative methods like numerical ranking,(More)
Advanced personalized web applications require a carefully dealing with their users’ wishes and preferences. Since such preferences do not always hold in general, personalized applications also have to consider the user’s current situation. In this paper we present a novel framework for modeling situations and situated preferences. Our approach consists of(More)
The Smart Data System (SDS) and its declarative query language, Declarative Reasoning, represent the first large-scale effort to commercialize deductive database technology. SDS offers the functionality of deductive reasoning in a distributed, heterogeneous database environment. In this article we discuss several interesting aspects of the query compilation(More)