Sketching a Model-Based Technique for Integrated Design and Run Time Description

Kästner, Andreas; Gogolla, Martin; Doan, Khanh-Hoang; Desai, Nisha

doi:10.1007/978-3-030-04771-9_39

Cited by 1 publication

(1 citation statement)

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“…At the same time, Model-Driven Engineering (MDE) (Brambilla et al 2017) approaches are a promising line for dealing with the complexity of designing CPS. However, in recent years the scope of MDE has been also extended to runtime aspects of CPS (Mazak & Wimmer 2016;Benelallam et al 2017;Cruz, Sadovykh, Truscan, Brunelière, et al 2020;Bencomo et al 2019;Gogolla et al 2019;Kästner et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Temporal Models on Time Series Databases.

Mazak¹,

Wolny²,

Gomez³

et al. 2020

JOT

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With the emergence of Cyber-Physical Systems (CPS), several 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 that support the storage of massive amounts of historical data as well as to provide fast query capabilities to 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, 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 four-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.

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