Showing papers on "Metadata repository published in 1992"

Metadata modeling and management

Abstract: The importance of information management stems from the need to control and utilize an organization's vast and vital information resources. In modern computerized enterprises, this need leads to the integration of information resources and systems--i.e., information integration. Metadata technology has been recognized in the literature as a cornerstone to information management and integration. As the vision of information integration evolves, the focus has shifted from simply managing information resources to enhancing functional synergy of business and/or production systems. This vision requires some basic extensions to previous metadata technology; namely, new metadata representation and management methods including explicitly both data resources and such knowledge resources as process models and business rules. Underlying this evolution of vision and technology is the progress from conventional data dictionary systems and NIST's Information Resources Dictionary System to the emerging repository and metadatabase models. Most previous metadata systems are limited to containing only data resource models (static metadata) for achieving data sharing in an enterprise. While this data sharing capability is an integral part of information integration, these systems are not poised to handle aspects that are considered vital to information integration; namely, contextual knowledge (dynamic metadata). The metadatabase model that has been developed at Rensselaer includes both classes of metadata and utilizes them as a new approach to information integration in heterogeneous and distributed environments. This dissertation presents a research effort that addresses some of the basic problems for the new metadata technology. Specifically, it develops the unified metadata representation and management methods according to the metadatabase model. These methods, in their own right, also contribute to some basic problems in the areas of data and knowledge engineering. The representation method contributes to connecting certain semantic data models with relational data structures, and then combining both with rule-based knowledge models. In a similar way, the metadata management method effects an integration of usual database techniques with certain knowledge-based technology in a generic sense. In particular, this research has achieved the following major results: (1) a Global Information Resources Dictionary (GIRD) model representing both static and dynamic metadata in a unified structure, (2) a rulebase model for knowledge representation, sharing, and management, (3) a metadata processing method combining the rulebase capability with database and file processing, and (4) a metadatabase management system (MDBMS) implementing these metadata methods for information resources management and information integration.

Semantic Metadata: Query Processing and Data Aggregation

Abstract: This paper outlines an approach to model semantic meta data such that statistical query processing is supported effectively. The model comprises both types of queries: checking the availability of data, and deriving formally specified target tables of aggregate data.

Semantic query processing in multidatabase systems: a logic-based approach

Abstract: A multidatabase system (MDBS) is a system that integrates the operational data of several autonomous database systems and provides a uniform interface and control mechanisms to control access to those data. To efficiently retrieve and manipulate the data stored in MDBS, a metadata dictionary is needed as a repository of essential information for reasoning, controlling, and maintaining the retrieval/manipulation processes. The authors develop a two-level active metadata dictionary approach based on logic for building a metadata dictionary, query processing, and maintenance in MDBS. The low-level metadata dictionaries (LLMDs) keep metadata for each corresponding local database in MDBS, respectively. The high-level metadata dictionary (HLMD) integrates the metadata about all LLMDs. >

Metadata and View Classification

Abstract: The discussion during the metadata and view session centered on the proper classification rules to be applied to each metadata level and the relationship of the user’s operating level and the metadata level on the base data level.