Extending ER models to capture database transformations to build data sets for data mining |
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| Affiliation: | 1. University of Houston, Texas 77204, USA;2. LaBRI, Bordeaux, France;1. Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, P.O. Box 5066, Dhahran 31261, Saudi Arabia;2. Computer Science and Engineering Department, Mississippi State University, Butler Hall, 665 George Perry St. Box 9637, MS 39762, USA;1. College of Engineering, Karachi Institute of Economics and Technology, Karachi 75190, Pakistan;2. College Computing and Information Sciences, Karachi Institute of Economics and Technology, Karachi 75190, Pakistan;3. School of Electrical, Electronic and Computer Engineering, The University of Western Australia, WA 6009, Australia;4. School of Computer Science and Software Engineering, The University of Western Australia, WA 6009, Australia;1. Institute of Industrial Technologies and Automation, ITIA-CNR, Via Bassini 15, Milan 20133, Italy;2. Institute of Applied Mathematics and Information Technology “Enrico Magenes”, IMATI-CNR, Via De Marini 6, Genoa 16149, Italy |
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| Abstract: | In a data mining project developed on a relational database, a significant effort is required to build a data set for analysis. The main reason is that, in general, the database has a collection of normalized tables that must be joined, aggregated and transformed in order to build the required data set. Such scenario results in many complex SQL queries that are written independently from each other, in a disorganized manner. Therefore, the database grows with many tables and views that are not present as entities in the ER model and similar SQL queries are written multiple times, creating problems in database evolution and software maintenance. In this paper, we classify potential database transformations, we extend an ER diagram with entities capturing database transformations and we introduce an algorithm which automates the creation of such extended ER model. We present a case study with a public database illustrating database transformations to build a data set to compute a typical data mining model. |
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