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Towards Integrated Data Analytics: Time Series Forecasting in DBMS

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Abstract

Integrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry in order to be able to cope with increasing data volume and increasing complexity of the analytical algorithms. One important statistical method is time series forecasting, which is crucial for decision making processes in many domains. The deep integration of time series forecasting offers additional advanced functionalities within a DBMS. More importantly, however, it allows for optimizations that improve the efficiency, consistency, and transparency of the overall forecasting process. To enable efficient integrated forecasting, we propose to enhance the traditional 3-layer ANSI/SPARC architecture of a DBMS with forecasting functionalities. This article gives a general overview of our proposed enhancements and presents how forecast queries can be processed using an example from the energy data management domain. We conclude with open research topics and challenges that arise in this area.

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Authors and Affiliations

  1. Technische Universität Dresden, Nöthnitzer Str. 46, 01187, Dresden, Germany

    Ulrike Fischer, Frank Rosenthal, Matthias Boehm & Wolfgang Lehner

  2. SAP Research Dresden, Chemnitzer Str. 48, 01187, Dresden, Germany

    Lars Dannecker

  3. Center for Data-intensive Systems, Aalborg University, Selma Lagerlöfs Vej 300, 9220, Aalborg, Denmark

    Laurynas Siksnys

Authors
  1. Ulrike Fischer
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  2. Lars Dannecker
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  3. Laurynas Siksnys
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  4. Frank Rosenthal
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  5. Matthias Boehm
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  6. Wolfgang Lehner
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Corresponding author

Correspondence to Ulrike Fischer.

Additional information

Matthias Boehm is currently visiting IBM Almaden Research Center, San Jose, CA, USA.

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Fischer, U., Dannecker, L., Siksnys, L. et al. Towards Integrated Data Analytics: Time Series Forecasting in DBMS. Datenbank Spektrum 13, 45–53 (2013). https://doi.org/10.1007/s13222-012-0108-4

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  • DOI: https://doi.org/10.1007/s13222-012-0108-4

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