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Ontology and SOA Based Data Mining to Business Process Optimization

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Information System Development

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Abstract

The need to improve business process efficiency, to react quickly to changes and to meet regulatory compliance is the main driver for using Business Process Intelligence (BPI). BPI refers to the application of Business Intelligence techniques, like data warehousing, data analysis, and data mining, to find correlations between different workflow aspects and performance metrics, to identify the causes of bottlenecks, and to find opportunities for business process prediction and optimization, e.g. elimination not necessary steps. In this paper we propose an ontology and Service Oriented Architecture (SOA) based approach for data mining process implementation for business processes optimization. The proposed approach was implemented in eight commercial companies, covering different industries, such as telecommunications, banking and retail. The experiment achieved shows that companies having data warehouse had a significant advantage, e.g. it allows us to eliminate not necessary operations and optimise business process.

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Notes

  1. 1.

    Here Data mining is understood as extracting or “mining” knowledge from large amounts of data in order to discover implicit, but potentially useful information.

  2. 2.

    http://www3.opengroup.org

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Author information

Authors and Affiliations

  1. Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia

    Aleksander Pivk

  2. Information Systems Department, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Olegas Vasilecas & Diana Kalibatiene

  3. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Rok Rupnik

Authors
  1. Aleksander Pivk
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  2. Olegas Vasilecas
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  3. Diana Kalibatiene
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  4. Rok Rupnik
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Corresponding author

Correspondence to Diana Kalibatiene .

Editor information

Editors and Affiliations

  1. Dept of Computer Languages and Systems, University of Seville, Seville, Spain

    María José Escalona

  2. IWT2 Research Group FIDETIA, University of Seville, Seville, Spain

    Gustavo Aragón

  3. Monash University, Caaulfield East, Victoria, Australia

    Henry Linger

  4. National University of Ireland, Galway, Ireland

    Michael Lang

  5. National University of Ireland, Galway, Ireland

    Chris Barry

  6. City University of Hong Kong, Kowloon, Hong Kong SAR

    Christoph Schneider

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© 2014 Springer International Publishing Switzerland

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Pivk, A., Vasilecas, O., Kalibatiene, D., Rupnik, R. (2014). Ontology and SOA Based Data Mining to Business Process Optimization. In: José Escalona, M., Aragón, G., Linger, H., Lang, M., Barry, C., Schneider, C. (eds) Information System Development. Springer, Cham. https://doi.org/10.1007/978-3-319-07215-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-07215-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07214-2

  • Online ISBN: 978-3-319-07215-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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