Querying with Intrinsic Preferences

Abstract

Abstract. The handling of user preferences is becoming an increasingly important issue in present-day information systems. Among others, preferences are used for information filtering and extraction to reduce the volume of data presented to the user. They are also used to keep track of user profiles and formulate policies to improve and automate decision making. We propose a logical framework for formulating preferences and its embedding into relational query languages. The framework is simple, and entirely neutral with respect to the properties of preferences. It makes it possible to formulate different kinds of preferences and to use preferences in querying databases. We demonstrate the usefulness of the framework through numerous examples.

Efficient Distributed Skylining for Web Information Systems

“…However, the semantic meaning of these (user provided) combining functions is unclear and users often have to guess the 'right' weightings for their query. The area of operations research and research in the field of human preferences like [6] or [8] has already since long criticized this lack in expressiveness.…”

“…The lack of expressiveness of this 'top k' query model, however, has first been addressed by [8] and with the growing incorporation of user preferences into database systems [6], [10] and information services [22] the limitations of the entire model became more and more obvious. This led towards the integration of so-called 'skyline queries' (e.g.…”

Multi-objective Query Processing for Database Systems

“…For the use in personalization in database retrieval and engineering of user preferences recent research in [7] and [13] has shown that these three operators (score aggregation, Pareto accumulation and lexicographic ordering) are the most common and essential constructors to build complex user preferences into queries allowing arbitrary combinations. Besides, also the closure of preference construction with these operators is shown for general partial orders.…”

“…For the same reasons we can safely output o 3 in K 2 after we have accessed o 6 . And after accessing o 7 we can output o 4 from K 1 (because it could only be dominated by o 2 in K 1 . However o 4 's score with respect to f 2 is strictly higher than o 2 's; thus it is not dominated by o 2 ).…”

“…As we have discussed in section 2 the only ways to handle multiple objectives today are given by complex preference frameworks like given in [7] or [13], since the top k (compensating all score lists) and skyline (compensating no score lists) approaches only accommodate the extreme cases. However, the actual algorithms given for evaluating complex preference queries up to now always create views containing the non dominated objects by accessing all database objects and compare pairwise for domination.…”