By Hans-Jörg Schek (auth.), Christian S. Jensen, Simonas à altenis, Keith G. Jeffery, Jaroslav Pokorny, Elisa Bertino, Klemens Böhn, Matthias Jarke (eds.)
This publication 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 offered 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, information mining, XML, complicated question processing, relocating items, allotted information, allotted processing, complex querying, XML-advanced querying, basic question providers, estimation/histograms, and aggregation.
Read 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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Extra info for Advances in Database Technology — EDBT 2002: 8th International Conference on Extending Database Technology Prague, Czech Republic, March 25–27, 2002 Proceedings
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 . For each predicate, the new weight is computed based on the similarity score of all attribute values for which relevance judgments exist. In section 3 we described how a Scores table is derived from the answer table and the feedback values. Using this information we develop two strategies: • Minimum Weight: use the minimum relevant similarity score for the predicate as the new weight.
Resume and job description are text descriptions, location is a two dimensional (latitude, longitude) position, and desired and oﬀered salary are numeric. Applicants and job listings are joined with a (similarity) condition to obtain the best matches. Unstated preferences in the initial condition may produce an undesirable ranking. A user then points out to the system a few desirable and/or undesirable examples where job location and the applicants home are close (short commute times desired); the system then modiﬁes the condition and produces a new ranking that emphasizes geographic proximity.
We used several similarity predicates and reﬁnement algorithms. For the ﬁrst experiment we implemented a similarity predicate and reﬁnement algorithm based on FALCON  for geographic locations, and a query point movement and dimension re-weighting for the pollution vector of the EPA dataset. We started with a conceptual query looking for a speciﬁc pollution proﬁle in the state of Florida. We executed the desired query and noted the ﬁrst 50 tuple as the ground truth. Next, we formulated this query in 5 diﬀerent ways, similar to what a user would do, retrieved only the top 100 tuples, and submitted tuple level feedback for those retrieved tuples that are also in the ground truth.