Adding intelligence to scientific data management
Computers in Physics
The software development environment as a knowledge base management system
Foundations of knowledge base management
Telos: representing knowledge about information systems
ACM Transactions on Information Systems (TOIS)
Data & Knowledge Engineering
Knowledge discovery in databases: an overview
AI Magazine
Representing, Analyzing, and Synthesizing Biochemical Pathways
IEEE Expert: Intelligent Systems and Their Applications
An Integrated Framework for Empirical Discovery
Machine Learning
Systems for Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
A Moose and a Fox Can Aid Scientists with Data Management Problems
DBLP-4 Proceedings of the Fourth International Workshop on Database Programming Languages - Object Models and Languages
Tioga: Providing Data Management Support
Tioga: Providing Data Management Support
To Support Global Change Research
To Support Global Change Research
The Sequoia 2000 Architecture and Implementation Strategy
The Sequoia 2000 Architecture and Implementation Strategy
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Over the last few years, dramatic increases and advances in mass storage for both secondary and tertiary storage made possible the handling of big amounts of data (for example, satellite data, complex scientific experiments, and so on). However, to the full use of these advances, metadata for data analysis and interpretation, as well as the complexity of managing and accessing large datasets through intelligent and efficient methods, are still considered to be the main challenges to the information-science community when dealing with large databases. Scientific data must be analyzed and interpreted by metadata, which has a descriptive role for the underlying data. Metadata can be, partly, a priori definable according to the domain of discourse under consideration (for example, atmospheric chemistry) and the conceptualization of the information system to be built. It may also be extracted by using learning methods from time-series measurement and observation data. In this paper, a knowledge-based management system (KBMS) is presented for the extraction and management of metadata in order to bridge the gap between data and information. The KBMS is a component of an intelligent information system based upon a federated architecture, also including a database management system for time-series-oriented data and a visualization system.