Conceptual declarative problem specification and solving in data intensive domains

Various notations aimed at defining the semantics of a computation in terms of the application domains have been experienced for conceptual modeling. For example, entity-relationship (ER) approach and UML (Unified Modeling Language) diagrams allow one to specify the semantics informally. Ontology languages based on description logic (DL) 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 multistructured data models gave rise to a need for their unified, integrated abstraction to make specifications independent of real data in data intensive domains (DID). To overcome these disadvantages, a novel approach for applying a combination of the semantically different declarative rule-based languages (dialects) for interoperable conceptual specifications over various rule-based systems (RSs) relying on the logic program transformation technique recommended by the W3C (World Wide Web Consortium) Rule Interchange Format (RIF) has been investigated. Such approach is coherently combined with the specification facilities aimed at the semantic rule-based mediation intended for the heterogeneous data base integration. The infrastructure implementing the multidialect conceptual specifications by the interoperable RSs and mediating systems (MSs) is introduced. The proof-of-concept prototype of the infrastructure based on the SYNTHESIS MS and RIF standard is presented. The approach for multidialect conceptualization of a problem domain, rule delegation, rule-based programs and mediators interoperability is explained in detail and illustrated on a real nondeterministic polynomial time (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 reusable data analysis in various application domains.