By Morzy Tadeusz, Patrick Valduriez, Ladjel Bellatreche
This ebook 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 provided have been conscientiously chosen and reviewed from one hundred thirty five submissions. The papers are prepared in topical sections corresponding 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; complicated 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
Otherwise, φ is added in tr(A[wa ]). Up to this point, for every valid constant CS ζ(U ) ∈ C, we ﬁnd a set of valid CDDs. t their CSs. However, there may exist implications among the CDDs mined for diﬀerent CSs. Therefore, further pruning is required on the set of all CDDs discovered to produce a minimal cover Σcε Fig 1. An example of a d-tree (line 7 of Algorithm 1). To eliminate these implied CDDs, we utilize the dominance relation among CSs. We introduce here, the concept of d-tree (dominance tree) for CSs: to capture the dominance relation among valid LHS CSs.
That is, we form all valid LHS cDFs for the tuples set supp(ζ(U ), r) in r that agrees on ζ(U ). The search space of possible LHS cDFs for even a single CS can be very large due the combinatorial distance intervals of DFs. Hence, the need for a user-speciﬁed constraint on the DFs in LHS cDFs and the deﬁnition of ε-minimal CDDs in Sect. 3. To ensure the discovery of ε-minimal CDDs, we mine CDDs with ε-DFs in their LHS cDFs. Naturally, we adapt and incorporate the technique in  for mining ε-DFs for this task (line 3 of Algorithm 1).
Hence, a tree-path p(φ) = A[wa ]/B1 [w1 ]/ · · · /B[wm ] represents a DD φ : B[w1 ] · · · B[wm ] → A[wa ]. Child nodes in the tree are sorted by their attribute and the distance intervals. The implication of DDs is detected via the notion of path-preﬁx . A path p(φ1 ) = A[wa ]/B1 [w1 ]/B2 [w2 ]/ · · · /Am [wm ] is a preﬁx of another path p(φ2 ) = ¯1 ]/B2 [w ¯2 ]/ · · · /Bk [w ¯k ] if m < k and for each i ∈ [1 · · · m], we have A[wa ]/B1 [w ¯i ]. Given two DDs φ1 , φ2 : φ1 implies φ2 if p(φ1 ) is a preﬁx of p(φ2 ).
Advances in Databases and Information Systems: 19th East European Conference, ADBIS 2015, Poitiers, France, September 8-11, 2015, Proceedings by Morzy Tadeusz, Patrick Valduriez, Ladjel Bellatreche