Storage Retrieval

Download Advanced Principles for Improving Database Design, Systems by Keng Siau, John Erickson PDF

By Keng Siau, John Erickson

Contemporary years have witnessed enormous leaps within the energy of database applied sciences, making a new point of strength to boost complex functions that upload price at exceptional degrees in all parts of data administration and usage. Parallel to this evolution is a necessity within the academia and for authoritative references to the examine during this zone, to set up a entire wisdom base that may allow the data expertise and managerial groups to achieve greatest merits from those thoughts.

Advanced rules for making improvements to Database layout, platforms Modeling, and software program improvement offers state-of-the-art study and research of the latest developments within the fields of database platforms and software program improvement. This e-book offers academicians, researchers, and database practitioners with an exhaustive number of experiences that, jointly, signify the kingdom of data within the box.

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We synthesize these concepts using the motivation of a real-life enterprise application to transform abstract concepts to explicit ontology terms, definitions, and constraints. A graphical overview of the ontology development steps in the methodology that Gruninger and Fox (1995) used to engineer the TOVE Measurement Ontology representations is shown in Figure 1. These steps involve (1) developing a motivating scenario, (2) designing informal competency questions to capture the scenario, (3) formalizing them to more formal competency questions, and (4) logically deducing the competency questions as a demonstration of competency.

The remedy to this seeming conundrum is to identify general concepts that cut across many domains—the domains’ “common-sense”—and collect them in a common-sense ontology (Lenat, 1995; Milton & Kazmierczak, 2004). This ontology is separated from more domain-specific ones. In fact, terms in several domain-specific ontologies can be defined using terms from a common general ontology. For example, a molecular biological ontology may provide building block representations for a biomedical ontology, which in turn underpins both gene and health care ontologies.

Gruber also outlines the conundrum of ontological commitment: the more one commits to represent a given domain in an ontology to make data more sharable for software applications closely associated with that domain’s needs, the less sharable the data becomes for other applications. For example, commitments made to develop a gene ontology useful for genomics applications render the ontology to be less likely to be used to share medical records data. Yet data sharing between genomics and patient care applications is critical in many situations.

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