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Towards Integrated Data Analytics: Time Series Forecasting in DBMS

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dc.contributor.authorFischer, Ulrike
dc.contributor.authorDannecker, Lars
dc.contributor.authorSiksnys, Laurynas
dc.contributor.authorRosenthal, Frank
dc.contributor.authorBoehm, Matthias
dc.contributor.authorLehner, Wolfgang
dc.date.accessioned2018-01-10T13:18:53Z
dc.date.available2018-01-10T13:18:53Z
dc.date.issued2013
dc.description.abstractIntegrating 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.
dc.identifier.pissn1610-1995
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/11666
dc.publisherSpringer
dc.relation.ispartofDatenbank-Spektrum: Vol. 13, No. 1
dc.relation.ispartofseriesDatenbank-Spektrum
dc.subjectArchitecture
dc.subjectChallenges
dc.subjectDBMS
dc.subjectTime series forecasting
dc.titleTowards Integrated Data Analytics: Time Series Forecasting in DBMS
dc.typeText/Journal Article
gi.citation.endPage53
gi.citation.startPage45

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Datenbank Spektrum 13(1) - März 2013