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By Martin Theobald, Luc De Raedt, Maximilian Dylla, Angelika Kimmig, Iris Miliaraki (auth.), Barbara Catania, Giovanna Guerrini, Jaroslav Pokorný (eds.)

This publication constitutes the completely refereed lawsuits of the seventeenth East-European convention on Advances in Databases and data platforms, ADBIS 2013, held in Genoa, Italy, in September 2013. The 26 revised complete papers awarded including 3 invited papers have been conscientiously chosen and reviewed from ninety two submissions. The papers are prepared in topical sections on ontologies; indexing; info mining; OLAP; XML info processing; querying; similarity seek; GPU; querying in parallel architectures; functionality overview; allotted architectures.

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Extra info for Advances in Databases and Information Systems: 17th East European Conference, ADBIS 2013, Genoa, Italy, September 1-4, 2013. Proceedings

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3 Quasi-Periodic Granularities We rely on the definition of granularity taken from the temporal database glossary [12] and adapted in [18], [19]. Such definitions are the basis for our treatment of periodic data. Definition 1. A granularity G quasi-periodically groups into a granularity H if: (i) G groups into H, and (ii) there exists a finite set of finite intervals E1 , . . , Ez and positive integers n and m, where n is less than the minimum of the number of granules of H, such that for all / and i + n < min(E), where i ∈ Z, if H(i) = kr=0 G( jr ) and H(i + n) = 0, E is the closest existing exception after H(i) (if such exception exists; otherwise E = max(k|H(k) = 0), / then H(i + n) = kr=0 G( jr + m).

The Algorithm 1 takes as input a implicit periodic relation r and a query period PQ and 36 B. Stantic gives as output a periodic relation containing all the tuples occurring during PQ , answer is provided in implicit representation. Only parts of the valid times that intersect with the query period PQ are reported in output. To perform implicit temporal range queries there is need to check for intersection and Algorithm 2 Check Periodic Intersection has as input a periodic tuple (PerID = NULL), and the query period PQ , and checks whether there is an intersection.

B) requires multiple comparisons between copies of elements (as well as making the removal of duplicate results necessary). Data-oriented partitioning on the other hand has the problem of overlap resulting in degraded performance, particularly on dense datasets. With TOUCH we want to combine the best of both, space- as well as dataoriented partitioning, while avoiding the pitfalls. We use data-oriented partitioning to avoid the replication problem of space-oriented partitioning and build an index based on data-oriented partitioning (similar to an R-Tree) on the first dataset A (all elements of A are in the leaf nodes).

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