Download Beyond Databases, Architectures, and Structures: 10th by Stanislaw Kozielski, Dariusz Mrozek, Pawel Kasprowski, PDF

By Stanislaw Kozielski, Dariusz Mrozek, Pawel Kasprowski, Bożena Malysiak-Mrozek, Daniel Kostrzewa

This e-book constitutes the refereed lawsuits of the tenth IEEE foreign convention past Databases, Architectures, and constructions, BDAS 2014, held in Ustron, Poland, in may well 2014. This booklet comprises fifty six conscientiously revised chosen papers which are assigned to eleven thematic teams: question languages, transactions and question optimization; info warehousing and massive facts; ontologies and semantic net; computational intelligence and knowledge mining; collective intelligence, scheduling, and parallel processing; bioinformatics and organic info research; picture research and multimedia mining; safety of database structures; spatial info research; purposes of database platforms; internet and XML in database systems.

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Read Online or Download Beyond Databases, Architectures, and Structures: 10th International Conference, BDAS 2014, Ustron, Poland, May 27-30, 2014. Proceedings PDF

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Additional info for Beyond Databases, Architectures, and Structures: 10th International Conference, BDAS 2014, Ustron, Poland, May 27-30, 2014. Proceedings

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Execution times of the query for total sales for a given month sliced into days Variant Small DB Medium DB Big DB plain 365 s 2 494 s > 2h aggr, based on mg inv 14 s 49 s 147 s aggr, based on mg pd 25 s 18 s 27 s invoices or by products on particular days. Tab. 3 presents the execution times of this query for various database instances and their variants without indices. 7. A query for eight best vendors of a given month SELECT vname , SUM( qty ∗ i l . 8. The rewritten query for eight best vendors of a given month SELECT vname , SUM( s u m q t y x p r i c e ) as s u m q t y x p r i c e FROM vend JOIN prod p USING( v i d ) JOIN mg pd USING( p i d ) WHERE EXTRACT( year FROM date ) = 2013 AND EXTRACT(month FROM date ) = 8 GROUP BY p .

NET framework, Entity Framework, NHibernate, MS SQL Server, PostgreSQL, performance analysis. 1 Introduction Most of the software applications which are nowadays developed are based on object-oriented programming languages and relational databases. Such approach allows creating applications which are built on the logical elements that interacts with each other, while the relational databases technology is responsible for preserving data structures in tabular forms with established relations. These two technologies, although they seems to be divergent, interact with each other in both theoretical and practical aspects.

C i d , SUM( s u m q t y x p r i c e ) FROM mg inv GROUP BY c u s t . c i d ORDER BY SUM( s u m q t y x p r i c e ) DESC LIMIT 2 0 ; @Granule (Dim = ” id , date , customer ” Agr = ”Sum( i n v L i n e s . q u a n t i t y ∗ i n v L i n e s . p r i c e ) ” ) @Granule (Dim = ” date , . . ” ) public c l a s s I n v o i c e { @DWDim private Long i d ; @DWDim private Date da te ; @DWDim private Customer customer ; @DWAggr( f u n c t i o n=”SUM( q u a n t i t y ∗ p r i c e ) ” ) private L i s t i n v L i n e s ; } Usually more than one subset of dimensions is used to aggregate data.

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