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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 structures, ADBIS 2013, held in Genoa, Italy, in September 2013. The 26 revised complete papers offered 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 facts processing; querying; similarity seek; GPU; querying in parallel architectures; functionality overview; disbursed architectures.

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1. 1 An Implicit Representation of Quasi-Periodic Granularities As a working example, let us consider the following user-defined granularity. Let us suppose that, in the year 2012, starting from Monday January 9th and ending on Sunday December 23rd, the ‘working shift’ for one employee in company is from 08:00 to 12:00, and from 14:00 to 18:00 each day from Monday to Friday, and from 08:00 to 12:00 on Saturday, let us call such a granularity ‘WS’. In addition, let us suppose that person also works on Saturday evening from 14:00 to 18:00 in two specific days, say on January 14th and 21st , let us call ‘WS+’ the granularity WS with such an addition.

In other words, besides time periods which repeats periodically in time, we also optionally add a set of periods that do not follow such periodic pattern. We propose the following implicit representation of a quasi-periodic granularity G. A quasi-periodic granularity G is represented by a quadruple: G = P, IP , IA , FT where P is an integer representing the duration of the periodic pattern; IP is the set of the convex periods in the first ‘periodic pattern’ of the bottom granularity; IA is the set of the convex periods constituting the aperiodic part; FT is a period constituting the frame time.

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). To avoid the issue of overlap, we do not probe the data-oriented index for every element of the second dataset B. Instead, we assign each element b of B to the lowest (closest to the leafs) internal node of the index that fully contains b.

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