Download Beyond Databases, Architectures and Structures: 11th 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 eleventh foreign convention entitled past Databases, Architectures and constructions, BDAS 2015, held in Ustroń, Poland, in may possibly 2015.

This e-book involves fifty three conscientiously revised chosen papers which are assigned to eight thematic teams: database architectures and function; facts integration, garage and information warehousing; ontologies and semantic net; man made intelligence, info mining and information discovery; photograph research and multimedia mining; spatial info research; database platforms improvement; program of database systems.

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S ←Solve(S) 6. if C(S) < C(Sbest ) then 7. Sbest ← S 8. counter ← 0 9. else 10. counter ← counter + 1 11. return (Sbest ) Fig. 1. The ifs schema previously imposed in any of the Solve steps. In general, the relaxation step can be executed according to different strategies. In the next section we first provide a formal definition of the Constraint Satisfaction Problem, as well as a description of how this paradigm has been used to represent and solve scheduling problems. 3 A CSP-Based Scheduling Problems Representation Constraint Programming (see Rossi et al.

Section 4 is devoted to the description of some results in the literature obtained utilizing the ifs against two difficult scheduling problem benchmarks. Finally, section 5 presents some conclusions. 2 The Iterative Flattening Search Schema Figure 1 introduces the generic ifs procedure. The algorithm basically implements an optimization loop, alternating relaxation and flattening steps as long as improved solutions are found, and terminates when a maximum number of non-improving iterations is reached.

Some of these techniques have been adapted to learning database ontologies too. RTAXON is such a learning technique that transforms statistically identified relational data into ontological relationships [11]. It takes both as input, the relational schema and the entity-relationship schema, and can discover data relations not specified in either schema, but found in the data [10]. Interesting is further a data mining example for learning fuzzy ontologies from unstructured data of a hyper-media database that can learn time-varying dynamics of the domain.

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