Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Data preparation for data mining
Data preparation for data mining
Building and managing the Meta Data Repository: A Full Life-Cycle Guide
Building and managing the Meta Data Repository: A Full Life-Cycle Guide
The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing and Deploying Data Warehouses with CD Rom
Description Logics in Data Management
IEEE Transactions on Knowledge and Data Engineering
What Are Ontologies, and Why Do We Need Them?
IEEE Intelligent Systems
Metadata Management for Data Warehousing: Between Vision and Reality
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
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Metadata-driven tools store control information in repositories that are outside of programs and applications. At runtime, this control information (i.e., metadata) is read, interpreted and dynamically bound into software execution. If new requirements arise, metadata may be changed without affecting the programs sharing it and without requiring re-compilation of these programs. Repositories store metadata according to a metadata structure, called a metamodel. M4 is the metamodel used by Mining Mart, a system for supporting data preprocessing for data mining. The aim of this paper is twofold. First, we introduce M4 (the MetaModel of Mining Mart) and present some ideas underlying the design and implementation. Second, we discuss on the basis of M4 issues related to metadata-driven software: advantages of building and using such software, its weaknesses and the role it plays for metadata management, especially within data warehousing environments.