Integration inconsistency removal in data mining

Julius Stuller

doi:10.1117/12.381743

6 April 2000 Integration inconsistency removal in data mining

Julius Stuller

Proceedings Volume 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II; (2000) https://doi.org/10.1117/12.381743
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

The technological progress in the areas of the hardware, specially in the field of the (secondary) memories where the ever increasing capacities are paradoxically in the last several years available at ever decreasing prices and smaller physical sizes, and the software, continuously more and more user friendly, efficient and cheaper, together with the general expansion of the computers to almost all human activities, make it easier to realize the integration of many already existing databases. Unfortunately the process of databases integration can be accompanied by many various difficulties and problems. One of them is surely the possible occurrence of the inconsistencies appearing in this process of the integration. As we will see these inconsistencies can occur at various levels and they can be of different types. At the next stage some users go even further and try to get more from the accumulated data through data mining techniques. A data warehouse can be considered as a suitable technology for this purpose. Having in mind the data mining view of a data warehouse, one needs to know the sources of possible inconsistencies when building such a data warehouse in order to eliminate them as much as possible. In the paper we will define several existence conditions under which can occur different types of the inconsistencies in a warehouse and we will propose a classification of these inconsistencies based on the their sources. We will also propose a methodology and a procedure both of which aim at the elimination of these inconsistencies.

Citation Download Citation

Julius Stuller "Integration inconsistency removal in data mining", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); https://doi.org/10.1117/12.381743

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