Temporal Models on Time Series Databases

Alexandra Mazak-Huemer, Sabine Wolny, Abel Gomez, Jordi Cabot, Manuel Wimmer, Gerti Kappel

Research output: Contribution to journalArticleResearchpeer-review


With the emergence of Cyber-Physical Systems (CPS), more and more sophisticated runtime monitoring solutions have been proposed in order to deal with extensive execution logs. One promising development in this respect is the integration of time series databases which allow to store massive amounts of historical data as well as to provide fast query capabilities to reason about runtime properties of CPS.
In this paper, we discuss how conceptual modeling can benefit from time series databases and vice versa. In particular, we present how metamodels and their instances, i.e., models, can be partially mapped to time series databases. Thus, the traceability between design and simulation/runtime activities can be ensured by retrieving and accessing runtime information, i.e., time series data, in design models. On this basis, the contribution of this paper is three-fold. First, a dedicated profile for annotating design models for time series databases is presented. Second, a mapping for integrating the metamodeling framework EMF with InfluxDB is introduced as a technology backbone enabling two distinct mapping strategies for model information. Third, we demonstrate how continuous time series queries can be combined with the Object Constraint Language (OCL) for
navigation through models, now enriched with derived runtime properties. Finally, we also present an initial evaluation of the different mapping strategies with respect to data storage and query performance. Our initial results show the efficiency of applying derived runtime properties as time series queries also for large model histories.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalJournal of object technology : JOT
Issue number3
Publication statusPublished - 2020


  • Runtime Models
  • Query Languages
  • Model-based Analysis
  • Temporal Modeling
  • Time Series Databases

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