By Morzy Tadeusz, Patrick Valduriez, Ladjel Bellatreche
This booklet constitutes the completely refereed complaints of the nineteenth East ecu convention on Advances in Databases and knowledge structures, 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 a hundred thirty five submissions. The papers are equipped in topical sections comparable to database idea and entry tools; consumer 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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Additional info for Advances in Databases and Information Systems: 19th East European Conference, ADBIS 2015, Poitiers, France, September 8-11, 2015, Proceedings
Example text
The first technique consists of adding local pivots to each node and having the distances of their elements from each additional pivot calculated and stored. The target for this technique is to reduce the uncertainty region and, consequently, the number of distance calculations by using the triangular inequality property in each node accessed by a query. The second technique consists of anticipating information from child nodes, such as distance and radius values, and use them to avoid unnecessary disk accesses.
Given an instance r of a relation schema R, the discovery problem of CDDs is to find a minimal cover of all CDDs that hold in r. Problem Statement. The search space of possible CDDs is large. To reduce the search space while discovering highly relevant CDDs, we restrict the maximum distance of DFs in the LHS cDFs of CDDs. Indeed, we incorporate the notion of ε-DFs [8] into CDD discovery and accordingly define ε-minimal CDDs. Let the function up(w) on the distance interval (w = [x, y]) of a DF A[w] return y.
Next, valid CDDs are formed with the LHS cDF (line 6 of Algorithm 1). Pruning and Generating a Minimal Cover. To ensure that the set of CDDs discovered for a given CS is (ε)-minimal and devoid of all implications, we adopt and extend the DD-Tree structure proposed in [11] for pruning implied CDDs. A DD-Tree, tr(A[wa ]), stores all DDs with the same RHS DF A[wa ]. Thus, the root of each DD-Tree is the common RHS A[wa ]. Other nodes in the tree are single-attribute DF of the LHS DFs of the DDs in the tree.
