By Morzy Tadeusz, Patrick Valduriez, Ladjel Bellatreche
This publication constitutes the completely refereed court cases of the nineteenth East eu convention on Advances in Databases and data platforms, ADBIS 2015, held in Poitiers, France, in September 2015. The 31 complete papers and 18 brief papers awarded have been rigorously chosen and reviewed from one hundred thirty five submissions. The papers are equipped in topical sections comparable to database conception and entry tools; person requisites and database evolution; multidimensional modeling and OLAP; ETL; transformation, extraction and archiving; modeling and ontologies; time sequence processing; functionality and tuning; complex question processing; approximation and skyline; confidentiality and trust.
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Extra resources for Advances in Databases and Information Systems: 19th East European Conference, ADBIS 2015, Poitiers, France, September 8-11, 2015, Proceedings
3 and 4 describe the two new proposed techniques; Sect. 5 presents how the techniques were applied to Slim-tree; Sect. 6 describes the experiments and discusses the results; and Sect. 7 presents the conclusions and future work. H. Oliveira et al. Background and Related Work Similarity Queries In order to allow performing queries over complex domains, the elements of a given complex dataset usually have features extracted from their content. The extracted features are used in place of the original data to execute the queries.
T. (ζ(A), A[wa ]) (ζ2 (A), A[w2 ]). The set Σ1 is a cover of the set Σ if every CDD in Σ is in or implied by DDs in Σ1 . Let Σ be a set of minimal CDDs. We say a cover Σc of Σ is minimal cover iﬀ. t. Σ ⊂ Σc . Conditional Diﬀerential Dependencies (CDDs) 9 Finding a minimal cover Σc of CDDs in a given instance of a relation is nontrivial and important. This is because, it presents a concise set of valid CDDs, making its utilization less costly. For instance, in data management applications that may require the validation of CDDs in data, a smaller Σc reduces the cost of validation, resulting in more eﬃcient and useful applications.
The coNP-hard bound is veriﬁed by the coNP-hardness of DDs. The coNP bound is veriﬁed by an NP algorithm that can verify if Σ |= σ. 3. Inference System for CDDs: An inference system allows pruning of implied rules during discovery. For example, the well-known Armstrong’s Axioms  form the bases of the ﬁnite axiomatizability of FDs and similar axioms have been derived f or CFDs and DDs. We present a few (because of space limit) of all the inference rules used in pruning in Sect. 4 below for CDDs.