Knowledge Discovery in Databases
Knowledge Discovery in Databases
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Communications of the ACM
An interactive visual query environment for exploring data
Proceedings of the 10th annual ACM symposium on User interface software and technology
Constructing OLAP cubes based on queries
Proceedings of the 4th ACM international workshop on Data warehousing and OLAP
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Internet management issues
Towards a Parallel Data Mining Toolbox
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Modelling Multidimensional Data in a Dataflow-Based Visual Data Analysis Environment
CAiSE '99 Proceedings of the 11th International Conference on Advanced Information Systems Engineering
A Toolbox Approach to Flexible and Efficient Data Mining
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Scalable Visual Hierarchy Exploration
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Coordinating declarative queries with a direct manipulation data exploration environment
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
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The analysis of business data is often an ill-defined task characterized by large amounts of noisy data. Because of this, business data analysis must combine two kinds of intertwined tasks: exploration and analysis. Exploration is the process of finding the appropriate subset of data to analyze, and analysis is the process of measuring the data to provide the business answer. While there are many tools available both for exploration and for analysis, a single tool or set of tools may not provide full support for these intertwined tasks. We report here on a project that set out to understand a specific business data analysis problem and build an environment to support it. The results of this understanding are, first of all, a detailed list of requirements of this task; second, a set of capabilities that meet these requirements; and third, an implemented client-server solution that addresses many of these requirements and identifies others for future work. Our solution incorporates several novel perspectives on data analysis and combines a history mechanism with a graphical, re-usable representation of the analysis and exploration process. Our approach emphasizes using the database itself to represent as many of these functions as possible.