By Martin Theobald, Luc De Raedt, Maximilian Dylla, Angelika Kimmig, Iris Miliaraki (auth.), Barbara Catania, Giovanna Guerrini, Jaroslav Pokorný (eds.)
This e-book constitutes the completely refereed lawsuits of the seventeenth East-European convention on Advances in Databases and knowledge structures, ADBIS 2013, held in Genoa, Italy, in September 2013. The 26 revised complete papers provided including 3 invited papers have been conscientiously chosen and reviewed from ninety two submissions. The papers are geared up in topical sections on ontologies; indexing; facts mining; OLAP; XML information processing; querying; similarity seek; GPU; querying in parallel architectures; functionality review; disbursed architectures.
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Extra resources for Advances in Databases and Information Systems: 17th East European Conference, ADBIS 2013, Genoa, Italy, September 1-4, 2013. Proceedings
ADVIS 2002. LNCS, vol. 2457, pp. 213–222. Springer, Heidelberg (2002) 22. : On caching and prefetching of virtual objects in distributed virtual environments. In: MULTIMEDIA 1998 (1998) 23. : Join Processing in Relational Databases. ACM Computing Surveys 24(1), 63–113 (1992) 24. : Algorithms in combinatorial geometry. Springer (1987) 25. : Spatial join techniques. au Abstract. Periodic events seem to be an intrinsic part of our life, and a way of perceiving reality. There are many application domains where periodic data play a major role.
In many of such domains, the huge number of repetitions make the goal of explicitly storing and accessing such data very challenging to the extent of even not being possible, in cases of open ended intervals. In this work, we present a concept to represent periodic data in an implicit way. The representation model we propose captures the notion of periodic granularity provided by the temporal database glossary. We define the algebraic operators, and introduce access algorithms to cope with them and also with temporal range queries, proving that they are correct and complete with respect to the traditional explicit approach.
Such a challenging task will be addressed in this work. , queries based on algebraic relational operators) on an implicit representation of periodic data. 32 B. Stantic Fig. 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’.