S. E. Madnick. From vldb to vmldb (very many large data bases): Dealing with large-scale semantic heterogenity. In VLDB, pages 11--16, 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....System B To integrate and automatically process these data we must resolve the heterogeneities on the modeling level. This requires explicit knowledge about the structure of and the semantic assumptions underlying the data. For the representation and exchange of this context information [4] we use metadata. 2 The interpretation of the metadata requires the introduction of a shared vocabulary to reach a common understanding with regard to a given domain. Such a vocabulary is provided by a domain specific ontology [2, 5] An explicit description of the relationships between the data ....

....A semantic object represents a data item together with its underlying semantic context, which consists of a variable set of meta attributes (also represented as semantic objects) that explicitly describe implicit modeling assumptions. Our approach is based on the notion of context as proposed in [7, 4]. A more comprehensive notion of context can be found in [3] In addition, each semantic object has a concept label associated with it that specifies the relationship between the object and the real world aspects it describes. These concept labels are taken from a commonly known ontology. Thus, ....

Madnick, S.E.: From VLDB to VMLDB (Very MANY Large Data Bases): Dealing with Large-Scale Semantic Heterogeneity, Proc. VLDB Conf., Zurich, Swizerland, 1995

....via relational or object oriented database systems many of the underlying modeling assumptions are only given implicitly, that is, they are in the minds of the designer, are specified in text documents not available externally, or are reflected in local applications. This context information [15] is lost when data is exchanged across institutional boundaries. Thus, to exchange and process data from independent participants in a semantically meaningful way, we need explicit information about its intended meaning. We use semantic metadata to represent this additional information. For ....

Madnick, S.E.: From VLDB to VMLDB (Very MANY Large Data Bases): Dealing with Large-Scale Semantic Heterogeneity, 21st Conf. on Very Large Data Bases, Zurich, Swizerland, 1995

....techniques [3] are related to the fact that semantic heterogeneity occurs on the large scale. This heterogeneity involves terminology, structure, and domain of the sources, with respect to geographical, organizational, 2 S. Bergamaschi et al. and functional aspects of the information use [28]. Moreover, to meet the requirements of global, Internet based information systems, it is important that the tools developed for supporting these activities are semi automatic and scalable as much as possible. To face the issues related to scalability in the large scale, in this paper we propose ....

S. E. Madnick. From vldb to vmldb (very many large data bases): Dealing with large-scale semantic heterogeneity. In VLDB Int. Conf., pages 11--16, 1995.

....heterogeneous Webbased data sources for further automatic processing. 2 Metadata for making structure and semantics explicit A meaningful exchange and a correct use of Web based data requires both information about its organization and meaning. This information, which we call context information [19, 12], provides the basis for determining the relationships between the data and the real world aspects it describes. For the explicit representation and exchange of this context information we use metadata. We distinguish between structural and semantic metadata. Structural metadata represents ....

.... e.g. data that describes the semantic content of a data value (like units of measure or scaling) or data that provides additional information about its creation (calculation algorithm or derivation formula used) data lineage (e.g. source) and quality (e.g. actuality and precision) [12]. A metadata model to describe context information in an unambiguous way is needed. We introduce domain specific conceptualizations, or ontologies [9] that provide a commonly agreed upon vocabulary to which data providers and consumers refer. Thus, an ontology serves as a common basis for the ....

Madnick, S.E.: From VLDB to VMLDB (Very MANY Large Data Bases): Dealing with Large-Scale Semantic Heterogeneity, Proc. 21st VLDB Conf., Zurich, Swizerland, 1995

.... dramatically due to the recent progress in network and database technology, which has produced distributed rather than centralised information systems [BRE90, HUR96] Thus new applications often involve accessing and maintaining data from several pre existing databases and software packages [BRE90, HUR96, MAD95, SEL93, VAS95], which are typically located on autonomous software and hardware platforms distributed over the many sites of a large computer network. This usually means that there are heterogeneity and legacy problems to be solved [DOG95, GAR96, KIM91a, KIM93, OZS91] 3 One approach to the above situation is ....

S. E. Madnick. From VLDB to VMLDB (Very MANY Large Databases): Dealing with Large-Scale Semantic Heterogeneity. In Proc. 21st VLDB Conference, pages 11--16, Zurich, Switzerland, 1995.

....of extracting globally accessible data using aggregation or selection operators. On the other side, when querying data in a multidatabase environment, semantic conflicts regarding different names, abstraction levels and structures have to be solved, called the context interchange problem in [19]. Global consistency constraints are also a kind of knowledge that goes beyond the local applications. The gap between the globally available data and the useful information may grow in the future and needs solutions incorporating knowledge about the local data. This knowledge can be implemented ....

S.E. Madnick; From VLDB to VMLDB (Very Many Large Databases): Dealing With Large Scale Semantic Heterogeneity, Proc. of the 21st International Conference on Very Large Data Bases (VLDB), Zurich, 1995.

....heterogeneous Webbased data sources for further automatic processing. 2 Metadata for making structure and semantics explicit A meaningful exchange and a correct use of Web based data requires both information about its organization and meaning. This information, which we call context information [19, 12], provides the basis for determining the relationships between the data and the real world aspects it describes. For the explicit representation and exchange of this context information we use metadata. We distinguish between structural and semantic metadata. Structural metadata represents ....

.... e.g. data that describes the semantic content of a data value (like units of measure or scaling) or data that provides additional information about its creation (calculation algorithm or derivation formula used) data lineage (e.g. source) and quality (e.g. actuality and precision) [12]. A metadata model to describe context information in an unambiguous way is needed. We introduce domain specific conceptualizations, or ontologies [9] that provide a commonly agreed upon vocabulary to which data providers and consumers refer. Thus, an ontology serves as a common basis for the ....

Madnick, S.E.: From VLDB to VMLDB (Very MANY Large Data Bases): Dealing with Large-Scale Semantic Heterogeneity, Proc. 21st VLDB Conf., Zurich, Swizerland, 1995

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S. E. Madnick. From vldb to vmldb (very many large data bases): Dealing with large-scale semantic heterogenity. In VLDB, pages 11--16, 1995.

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