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Data integration points

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Cruzcharles

Introduction 

Identifying integration points helps you to pinpoint the technological requirements related to your process model, which you need to know before you can configure the appropriate components to execute the work. Be specific in naming the applications that must be integrated. A data integration point refers to the data elements that comprise a data entity, such as an account data entity, that are synchronized between Siebel CRM and Oracle FLEXCUBE Universal Banking. Integration points are points at which documents within your process move from one system or application to another. Communication Adapters for Integration. Adapters perform activities that control the transmission of business process data between Sterling B2B Integrator and external applications.

Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). The effectiveness of ontology‑based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process.

Under a Creative Commons license Open access. 

Abstract

The Ontology-Based Data Access (OBDA) systems allows users to access external databases through a conceptual domain view, given in terms of an ontology. This semantic technology addresses such problems as conceptual modeling, query rewriting, an source-to-target mapping. We show how these issues have been defined and implemented in the DAFO system, highlighting those features that distinguish it from the standard OBDA system. We propose an original approach to cope with the trade-off between expressiveness of the ontology-based conceptual modelling and ontology-mediated query rewriting in the DAFO systems. For this purpose, we divide ontological rules into three parts: (a) rewriting rules, (b) rules defining intensional predicates (views), and (c) constraint rules (satisfied in the database). This allows to balance the semantic power of the conceptual modeling and the effciency of query answering in the DAFO system, where queries are based on the faceted query paradigm. 

An ontology-based data integration(OBDI) system is an information management system consisting of three components: an ontology, a set of data sources, and the mapping between the two. The ontology is a conceptual, formal description of the domain of interest to a given organization (or a community of users), expressed in terms of relevant concepts, attributes of concepts, relationships between concepts, and logical assertions characterizing the domain knowledge. The data sources are the repositories accessible by the organization where data concerning the domain are stored. In the general case, such repositories are numerous, heterogeneous, each one managed and maintained independently from the others. The mapping is a precise specification of the correspondence between the data contained in the data sources and the elements of the ontology. The main purpose of an OBDI system is to allow information consumers to query the data using the elements in the ontology as  process of combining data from different sources into a single, unified view. Integration begins with the ingestion process, and includes steps such as cleansing, ETL mapping, and transformation. 

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