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 complaints 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 awarded 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; info mining; OLAP; XML information processing; querying; similarity seek; GPU; querying in parallel architectures; functionality evaluate; allotted architectures.
Read Online or Download Advances in Databases and Information Systems: 17th East European Conference, ADBIS 2013, Genoa, Italy, September 1-4, 2013. Proceedings PDF
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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
With this insight, we develop a two phased approach where we (1) ﬁnd an arbitrary element e in the query range and (2) recursively retrieve all other elements (the neighbors of e) within the range. , what element is next to what other elements. Both phases are independent of overlap and density as the ﬁrst only depends on the height of the R-tree-like
structure and the sec
ond only depends on the number of elements in the range query.
Tauheed et al. The experiment shown in Figure 2 where we increase the density of the dataset and measure point query performance of diﬀerent R-tree based approaches [16,17,18] clearly demonstrates the problem of overlap. Point queries are a good indicator of overlap in R-Trees: the number of nodes retrieved from disk should be in the order of the height of the tree (ﬁve in this case) in the absence of overlap. As the results in Figure 2 show, however, the overlap grows rapidly with increasing dataset density, translating into a higher execution time and thus degraded performance.
This means that for every tuple in t ∈ QExp there is not tuple in Imp such that the tuple in Imp generated the tuple t, but the tuple in Imp does not satisfy the query. V Te , and the conditions for the explicit query and the condition in the algorithm for the implicit query are the same. per < P ; otherwise s satisfies the condition to be included in QImp , contrary to the assumption. 8 Conclusions In this work, we presented a new approach to cope efficiently with periodic data in relational databases.
Advances in Databases and Information Systems: 17th East European Conference, ADBIS 2013, Genoa, Italy, September 1-4, 2013. Proceedings by Martin Theobald, Luc De Raedt, Maximilian Dylla, Angelika Kimmig, Iris Miliaraki (auth.), Barbara Catania, Giovanna Guerrini, Jaroslav Pokorný (eds.)