Typescript (photocopy). Thesis (M.S.)--Miami University, Dept. of Systems Analysis, 1998. Include... more Typescript (photocopy). Thesis (M.S.)--Miami University, Dept. of Systems Analysis, 1998. Includes bibliographical references (leaves 107-111).
The web is undoubtedly the largest and most diverse repository of data, but it was not designed t... more The web is undoubtedly the largest and most diverse repository of data, but it was not designed to offer the capabilities of traditional data base management systems – which is unfortunate. In a true data federation, all types of data sources, such as relational databases and semi-structured websites, could be used together. IBM WebSphere uses the “request-reply-compensate ” protocol to communicate with wrappers in a data federation. This protocol expects wrappers to reply to query requests by indicating the portion of the queries they can answer. While this provides a very generic approach to data federation, it also requires the wrapper developer to deal with some of the complexities of capability considerations through custom coding. Alternative approaches based on declarative capability restrictions have been proposed in the literature, but they have not found their way into commercial systems, perhaps due to their complexity. We offer a practical middle-ground solution to query...
Introduction The popularity and growth of the Web have dramatically increased the number of infor... more Introduction The popularity and growth of the Web have dramatically increased the number of information sources available for use and the opportunity for important new information-intensive applications (e.g., massive data warehouses, integrated supply chain management, global risk management, intransit visibility). Unfortunately, there are significant challenges to be overcome regarding data interpretation. Specifically, the existence of heterogeneous contexts, whereby each source of information and potential receiver of that information may operate with a different context, leading to large-scale semantic heterogeneity. A context is the collection of implicit assumptions about the meaning of data. As a simple example, whereas most US universities grade on a 4.0 scale, MIT uses a 5.0 scale -- posing a problem if one is comparing student GPA's. Another typical example might be the extraction of price information from the Web: but is the price in Dollars or Yen (If dollars, is it...
The prospect of combining information from diverse sources for superior decision making is plague... more The prospect of combining information from diverse sources for superior decision making is plagued by the challenge of semantic heterogeneity, as data sources often adopt different conventions and interpretations when there is no coordination. An emerging solution in information integration is to develop an ontology as a standard data model for a domain of interest, and then to define the correspondences between the data sources and this common model to eliminate their semantic heterogeneity and produce a single integrated view of the data sources. We first claim that this single integrated view approach is unnecessarily restrictive, and instead offer the view that ontologies can simultaneously accommodate multiple integrated views provided the accompaniment of contexts, a set of axioms on the interpretation of data allowing local variations in representation and nuances in meaning, and a conversion function network between contexts to reconcile contextual differences. Then, we illustrate how to achieve semantic interoperability between multiple ontology-based applications. During this process, application ontologies are aligned through the reconciliation of their context models, and a new application with a virtual merged ontology is created. We illustrate this alternative approach with the alignment of air travel and car rental domains, an actual example from our prototype implementation.
Caméléon# is a web data extraction and management tool that provides information aggregation with... more Caméléon# is a web data extraction and management tool that provides information aggregation with advanced capabilities that are useful for developing value-added applications and services for electronic business and electronic commerce. To illustrate its features, we use an airfare aggregation example that collects data from eight online sites, including Travelocity, Orbitz, and Expedia. This paper covers the integration of Caméléon# with commercial database management systems, such as MS SQL Server, and XML query languages, such as XQuery.
Typescript (photocopy). Thesis (M.S.)--Miami University, Dept. of Systems Analysis, 1998. Include... more Typescript (photocopy). Thesis (M.S.)--Miami University, Dept. of Systems Analysis, 1998. Includes bibliographical references (leaves 107-111).
The web is undoubtedly the largest and most diverse repository of data, but it was not designed t... more The web is undoubtedly the largest and most diverse repository of data, but it was not designed to offer the capabilities of traditional data base management systems – which is unfortunate. In a true data federation, all types of data sources, such as relational databases and semi-structured websites, could be used together. IBM WebSphere uses the “request-reply-compensate ” protocol to communicate with wrappers in a data federation. This protocol expects wrappers to reply to query requests by indicating the portion of the queries they can answer. While this provides a very generic approach to data federation, it also requires the wrapper developer to deal with some of the complexities of capability considerations through custom coding. Alternative approaches based on declarative capability restrictions have been proposed in the literature, but they have not found their way into commercial systems, perhaps due to their complexity. We offer a practical middle-ground solution to query...
Introduction The popularity and growth of the Web have dramatically increased the number of infor... more Introduction The popularity and growth of the Web have dramatically increased the number of information sources available for use and the opportunity for important new information-intensive applications (e.g., massive data warehouses, integrated supply chain management, global risk management, intransit visibility). Unfortunately, there are significant challenges to be overcome regarding data interpretation. Specifically, the existence of heterogeneous contexts, whereby each source of information and potential receiver of that information may operate with a different context, leading to large-scale semantic heterogeneity. A context is the collection of implicit assumptions about the meaning of data. As a simple example, whereas most US universities grade on a 4.0 scale, MIT uses a 5.0 scale -- posing a problem if one is comparing student GPA's. Another typical example might be the extraction of price information from the Web: but is the price in Dollars or Yen (If dollars, is it...
The prospect of combining information from diverse sources for superior decision making is plague... more The prospect of combining information from diverse sources for superior decision making is plagued by the challenge of semantic heterogeneity, as data sources often adopt different conventions and interpretations when there is no coordination. An emerging solution in information integration is to develop an ontology as a standard data model for a domain of interest, and then to define the correspondences between the data sources and this common model to eliminate their semantic heterogeneity and produce a single integrated view of the data sources. We first claim that this single integrated view approach is unnecessarily restrictive, and instead offer the view that ontologies can simultaneously accommodate multiple integrated views provided the accompaniment of contexts, a set of axioms on the interpretation of data allowing local variations in representation and nuances in meaning, and a conversion function network between contexts to reconcile contextual differences. Then, we illustrate how to achieve semantic interoperability between multiple ontology-based applications. During this process, application ontologies are aligned through the reconciliation of their context models, and a new application with a virtual merged ontology is created. We illustrate this alternative approach with the alignment of air travel and car rental domains, an actual example from our prototype implementation.
Caméléon# is a web data extraction and management tool that provides information aggregation with... more Caméléon# is a web data extraction and management tool that provides information aggregation with advanced capabilities that are useful for developing value-added applications and services for electronic business and electronic commerce. To illustrate its features, we use an airfare aggregation example that collects data from eight online sites, including Travelocity, Orbitz, and Expedia. This paper covers the integration of Caméléon# with commercial database management systems, such as MS SQL Server, and XML query languages, such as XQuery.
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