By Hans-Jörg Schek (auth.), Christian S. Jensen, Simonas à altenis, Keith G. Jeffery, Jaroslav Pokorny, Elisa Bertino, Klemens Böhn, Matthias Jarke (eds.)
This e-book constitutes the refereed court cases of the eighth overseas convention on Extending Database expertise, EDBT 2002, held in Prague, Czech Republic, in March 2002.
The 36 revised complete papers awarded including six commercial and alertness papers, thirteen software program demos and one invited paper have been conscientiously reviewed and chosen from a complete of 207 submissions. The papers are geared up in topical sections on question transformation, facts mining, XML, complicated question processing, relocating gadgets, disbursed facts, allotted processing, complicated querying, XML-advanced querying, basic question companies, estimation/histograms, and aggregation.
Read Online or Download Advances in Database Technology — EDBT 2002: 8th International Conference on Extending Database Technology Prague, Czech Republic, March 25–27, 2002 Proceedings PDF
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Additional resources for Advances in Database Technology — EDBT 2002: 8th International Conference on Extending Database Technology Prague, Czech Republic, March 25–27, 2002 Proceedings
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.
The qualitative approach is strictly more general than the quantitative one, since one can deﬁne preference relations in terms of scoring functions (if the latter are explicitly given), while not every intuitively plausible preference relation can be captured by scoring functions. Example 2. There is no scoring function that captures the preference relation described in Example 1. Since there is no preference deﬁned between any of the ﬁrst three tuples and the fourth one, the score of the fourth tuple should be equal to all of the scores of the ﬁrst three tuples.
The goal of inter-predicate reﬁnement via query re-weighting is to ﬁnd the optimal relative weights among diﬀerent predicates used in the scoring rule. , sn their similarity scores, and v1opt , v2opt , . . , sn , vnopt ) captures the user’s notion of similarity of a tuple to the users query. Let v10 , v20 , . . , vn0 be the initial weights associated with the scoring rule (for simplicity start with equal weights for all predicates). Reweighting modiﬁes these weights based on the user’s feedback to converge them to the optimal weights: limk−→∞ vik = viopt .