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An OLAP Rule Environment for Reactive Performance Monitoring

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E-Technologies: Embracing the Internet of Things (MCETECH 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 289))

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Abstract

Organizations are increasingly focused on using data to make their operational business processes more intelligent and reactive. Reactive performance monitoring consists of automating strategic decisions into rule-based actions when appropriate. Often there is a response gap, where there is unnecessary latency or missing detail, as the mapping between the strategic, the operational and the analytical is complex, labor intensive and not clearly defined. This paper presents an OLAP-modeled rule environment that leverages Complex Event Processing (CEP), multi-dimensionally modeled On-line Analytical Processing (OLAP) databases and Business Process Management (BPM) to provide reactive performance monitoring. We use two case studies to evaluate our proposed architecture for an OLAP-modeled rule environment. The first case study prototyped the environment using IBM PMQ, while the second prototyped the environment using QuickForms and a custom-built OLAP rule engine. Predictive modeling, traditional rule engines, and our OLAP rule engine are compared in terms of their support for reactive performance monitoring.

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Authors and Affiliations

  1. University of Ottawa, Ottawa, Canada

    Katherine Chengli & Liam Peyton

Authors
  1. Katherine Chengli
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  2. Liam Peyton
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Corresponding author

Correspondence to Liam Peyton .

Editor information

Editors and Affiliations

  1. Université de Montréal, Montreal, Québec, Canada

    Esma Aïmeur

  2. University of Ottawa, Ottawa, Ontario, Canada

    Umar Ruhi

  3. Carleton University, Ottawa, Ontario, Canada

    Michael Weiss

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© 2017 Springer International Publishing AG

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Chengli, K., Peyton, L. (2017). An OLAP Rule Environment for Reactive Performance Monitoring. In: Aïmeur, E., Ruhi, U., Weiss, M. (eds) E-Technologies: Embracing the Internet of Things . MCETECH 2017. Lecture Notes in Business Information Processing, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-319-59041-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-59041-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59040-0

  • Online ISBN: 978-3-319-59041-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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