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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXI pp 73–93Cite as

Modular Neural Networks for Extending OLAP to Prediction

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Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9260))

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 to assist it in facing up the challenges arising from the particularity of OLAP cubes. It consists of an OLAP oriented cube exploration strategy coupled with a dimensions reduction step that reposes on the Principal Component Analysis (PCA). Carried out experiments highlight the efficiency of MCEF in assisting the application of MNNs on OLAP cubes and the high predictive capabilities of NAP-HC.

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Author information

Authors and Affiliations

  1. Faculty of Sciences of Tunis, University of Tunis El-Manar, LIPAH-LR 11ES14, 2092, Tunis, Tunisia

    Wiem Abdelbaki & Sadok Ben Yahia

  2. Department of Information Systems, College of Economics Management and Information Systems, University of Nizwa, 616 Nizwa, Nizwa, Sultanate of Oman

    Wiem Abdelbaki

  3. Faculty of Economics and Management of Nabeul, University of Carthage, 8000, Nabeul, Tunisia

    Riadh Ben Messaoud

Authors
  1. Wiem Abdelbaki
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  2. Sadok Ben Yahia
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  3. Riadh Ben Messaoud
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Corresponding author

Correspondence to Wiem Abdelbaki .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. ICAR-CNR and University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  5. Hewlett-Packard Labatories, Palo Alto, California, USA

    Umeshwar Dayal

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Abdelbaki, W., Yahia, S.B., Messaoud, R.B. (2015). Modular Neural Networks for Extending OLAP to Prediction. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXI. Lecture Notes in Computer Science(), vol 9260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47804-2_4

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  • DOI: https://doi.org/10.1007/978-3-662-47804-2_4

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