A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
Superviews: Virtual Integration of Multiple Databases
IEEE Transactions on Software Engineering
A Theory of Attributed Equivalence in Databases with Application to Schema Integration
IEEE Transactions on Software Engineering
MDAS: Multiple schema integration approach
Data Engineering
Federated database systems for managing distributed, heterogeneous, and autonomous databases
ACM Computing Surveys (CSUR) - Special issue on heterogeneous databases
Using semantic values to facilitate interoperability among heterogeneous information systems
ACM Transactions on Database Systems (TODS)
Vertical information management: a framework to support high-level information requests in the context of autonomous information systems
Object Database Standard: ODMG-93
Object Database Standard: ODMG-93
The Inter-Database Instance Identification Problem in Integrating Autonomous Systems
Proceedings of the Fifth International Conference on Data Engineering
ViewSystem: Integrating Heterogeneous Information Bases by Object-Oriented Views
Proceedings of the Sixth International Conference on Data Engineering
Object Exchange Across Heterogeneous Information Sources
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
BNCOD 10 Proceedings of the 10th British National Conference on Databases: Advanced Database Systems
RIDE '96 Proceedings of the 6th International Workshop on Research Issues in Data Engineering (RIDE '96) Interoperability of Nontraditional Database Systems
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Vertical information management (VIM) is a term coined to describe a particular set of information management activities. These activities support decision makers working within various levels of a management hierarchy, who seek information from potentially large, distributed, heterogeneous, and federated information sources. Decision makers usually require information beyond what is stored. Yet, the collected data is a valuable resource. This is particularly important for scientific experimental results where the samples are expensive to collect and analyze, as in environmental remediation and restoration. One sample from a storage tank containing nuclear waste can cost over $1,000,000. A fundamental assumption of this work is that high-level information requests may involve data that is extracted or derived from underlying information sources, as well as data that is not present in the underlying information sources (referred to as ``gaps''). We observe that current practice often involves manual processing and negotiation to select relevant information and to fill gaps. We present a VIM framework for the specification, refinement, and partitioning of a high-level information request resulting in the extraction, collection, aggregation, and abstraction of the underlying data. This framework captures the specification of the information and the summarization steps used in a highly manual process to leverage the investment against future information requests. This work has been supported, in part, by the Department of Energy's Pacific Northwest National Laboratory.