Full Citation

Title: Modular Neural Networks for Extending OLAP to Prediction

Citation Type: Book, Section

Publication Year: 2015

ISBN:

ISSN:

DOI: 10.1007/978-3-662-47804-2_4

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Abstract: On-line Analytical Processing (OLAP) represents a good applications package to explore and navigate into data cubes. Though, it is limited to exploratory tasks. It does not assist the decision maker in performing information investigation. Thus, various studies have been trying to extend OLAP to new capabilities by coupling it with data mining algorithms. Our current proposal stands within this trend. It has two major contributions. First, a Multi-perspectives Cube Exploration Framework (MCEF) is introduced. It is a generalized framework designed to assist the application of classical data mining algorithm on OLAP cubes. Second, a Neural Approach for Prediction over High-dimensional Cubes (NAP-HC) is also introduced, which extends Modular Neural Networks (MNN)s architecture to multidimensional context of OLAP cubes, to predict non-existent measures. A preprocessing stage is embedded in NAP-HC . . .

Url: http://link.springer.com/10.1007/978-3-662-47804-2_4

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Authors: Abdelbaki, Wiem; Yahia, Sadok Ben; Messaoud, Riadh Ben

Editors:

Pages: 73-93

Volume Title: Transactions on Large-Scale Data- and Knowledge-Centered Systems XXI

Publisher: Springer, Berlin, Heidelberg

Publisher Location: Berlin, Heidelberg

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Edition:

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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