Temporal Models on Time Series Databases


With the emergence of Cyber-Physical Systems (CPS), several sophisticated runtime monitoring solutions havebeen proposed in order to deal with extensive execution logs. One promising development in this respect is the integration oftime series databases that support the storage of massive amounts of historical data as well as to provide fast query capabilitiesto reason about runtime properties of such 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, thetraceability 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 four-fold. First, a dedicated profilefor annotating design models for time series databases is presented. Second, a mapping for integrating the metamodelingframework EMF with InfluxDB is introduced as a technology backbone enabling two distinct mapping strategies for modelinformation. 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 evaluationof the different mapping strategies with respect to data storage and query performance. Our initial results show the efficiency ofapplying derived runtime properties as time series queries also for large model histories.

Journal of Object Technology, 19 (2020), 3; 1 - 15
Alexandra Mazak
Projektass. Dipl.-Ing. Mag.rer.soc.oec. Dr.techn.
Sabine Wolny
Projektass. Dipl.-Ing.
Manuel Wimmer
Privatdoz. Mag.rer.soc.oec. Dr.rer.soc.oec.
Gerti Kappel
Gerti Kappel
O.Univ.Prof.in Dipl.-Ing.in Mag.a Dr.in techn.