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Conceptual Declarative Problem Specification and Solving

Conceptual Declarative Problem Specification and Solving in Data Intensive Domains.

Author(s): Kalinichenko L. A., Stupnikov S. A., Vovchenko E. A., Kovalev D. Y.
Published:Informatics and Applications. Moscow: IPI RAN, 2013. -- V. 7, Iss. 4. -- P. 112-139.
Various notations aimed at defining the semantics of a computation in terms of the application domains have been experienced for conceptual modeling. E.g., entity-relationship approach and UML diagrams allow one to specify the semantics informally. Ontology languages based on description logic have been developed to formalize the semantics of data. However, it is now generally acknowledged that data semantics alone are insufficient and still representation of data analysis algorithms is necessary to specify data and behavior semantics in one paradigm. Moreover, the curse of ever increasing diversity of multi-structured data models gave rise to a need for their unified, integrated ab-straction to make specifications independent of real data in data intensive do-mains. To overcome these disadvantages a novel approach for applying a combination of the semantically different declarative rule-based languages (dialects) for in-teroperable conceptual specifications over various rule-based systems (RSs) re-lying on the logic program transformation technique recommended by the W3C Rule Interchange Format (RIF) has been investigated. Such approach is coher-ently combined with the specification facilities aimed at the semantic rule-based mediation intended for the heterogeneous data base integration. The infrastructure implementing the multi-dialect conceptual specifications by the interoperable RSs and mediating systems (MSs) is introduced. The proof-of-concept prototype of the infrastructure based on the SYNTHESIS mediating system and RIF standard is presented. The approach for multi-dialect conceptualization of a problem domain, rule delegation, rule-based programs and mediators interoperability is explained in detail and illustrated on a real NP-complete use-case in the finance domain. The research results are promising for the usability of the approach and of the infrastructure for conceptual, declarative, resource independent and re-usable data analysis in various application domains.
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