By Stanislaw Kozielski, Dariusz Mrozek, Pawel Kasprowski, Bożena Malysiak-Mrozek, Daniel Kostrzewa
This publication 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 e-book comprises fifty six rigorously revised chosen papers which are assigned to eleven thematic teams: question languages, transactions and question optimization; facts warehousing and massive info; ontologies and semantic net; computational intelligence and information mining; collective intelligence, scheduling, and parallel processing; bioinformatics and organic facts research; picture research and multimedia mining; safety of database structures; spatial information research; purposes of database structures; internet and XML in database systems.
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Additional info for Beyond Databases, Architectures, and Structures: 10th International Conference, BDAS 2014, Ustron, Poland, May 27-30, 2014. Proceedings
If the GROUP node has no children, remove it. Replace columns and aggregates that are members of the metagranule with columns from the table that stores the aggregated data of the metagranule. 6. Remove tables from the FROM node that are no longer referenced. 7. Add the table that stores the data of the metagranule to the FROM node. 6 Performance We used a computer with Intel i5 3570 (ivy bridge) and 8GiB RAM. The storage was Raid 0 over 4xBlack Caviar 1TB controlled by Adaptec 2405. 10. 1. We tested against three database instances: small (base data of size 34 GiB), medium (130 GiB) and big (345 GiB).
For the sake of readability we present them in SQL, since there can be interested readers who are not familiar with HQL. 4. A query for ﬁve the best invoices on a given day SELECT name , SUM( qty ∗ p r i c e ) AS s u m q t y x p r i c e FROM i n v l i n e JOIN i n v USING ( i n v i d ) JOIN c u s t USING ( c i d ) WHERE date = ’ 2 0 1 3 . 1 1 . 5. The optimized query for ﬁve the best invoices on a given day SELECT date , name , s u m q t y x p r i c e FROM mg inv JOIN c u s t USING ( c i d ) WHERE date = ’ 2 0 1 3 .
Fig. 2. DTP architecture overview 24 4 M. Iwaniak and W. Khadzhynov CPN Model of DTP Environment with Single AP To create our CPN model we have used PIPE modeling tool. Strong sides of PIPE are its constant developed, intuitive model creation and animation mode in which we can ﬁre enabled transitions and observe marking changes. Most of analysis and simulation modules of PIPE works only for Ordinary Petri Nets with one color deﬁne and are useless for CPN analysis. Fig. 3 presents our base CPN model and its places are described by Tab.