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International Workshop on Machine Learning, Optimization and Big Data

MOD 2015: Machine Learning, Optimization, and Big Data pp 306–317Cite as

Conceptual Analysis of Big Data Using Ontologies and EER

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9432))

Abstract

Large amounts of “big data” are generated every day, many in a “raw” format that is difficult to analyze and mine. This data contains potential hidden meaningful concepts, but much of the data is superfluous and not of interest to the domain experts. Thus, dealing with big raw data solely by applying a set of distributed computing technologies (e.g., MapReduce, BSP [Bulk Synchronous Parallel], and Spark) and/or distributed storage systems, namely NoSQL, is generally not sufficient. Extracting the full knowledge that is hidden in the raw data is necessary to efficiently enable analysis and mining. The data needs to be processed to remove the superfluous parts and generate the meaningful domain-specific concepts. In this paper, we propose a framework that incorporates conceptual modeling and EER principle to effectively extract conceptual knowledge from the raw data so that mining and analysis can be applied to the extracted conceptual data.

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Author information

Authors and Affiliations

  1. Geo-Informatics and Space Technology Development Agency (Public Organization), Ministry of Science and Technology of Thailand, 120 The Government Complex, Chaeng Wattana Road, Lak Si, Bangkok, 10210, Thailand

    Kulsawasd Jitkajornwanich

  2. Department of Computer Science and Engineering, The University of Texas at Arlington, 701 S Nedderman Dr, Arlington, TX, 76019, USA

    Ramez Elmasri

Authors
  1. Kulsawasd Jitkajornwanich
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  2. Ramez Elmasri
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Corresponding author

Correspondence to Kulsawasd Jitkajornwanich .

Editor information

Editors and Affiliations

  1. University of Florida, Gainsville, Florida, USA

    Panos Pardalos

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

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© 2015 Springer International Publishing Switzerland

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Jitkajornwanich, K., Elmasri, R. (2015). Conceptual Analysis of Big Data Using Ontologies and EER. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_27

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  • DOI: https://doi.org/10.1007/978-3-319-27926-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27925-1

  • Online ISBN: 978-3-319-27926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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