Reflecting on the past and the present with temporal graph-based models

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Abstract

Self-adaptive systems (SAS) need to reflect on the current environment conditions, their past and current behaviour to support decision making. Decisions may have different effects depending on the context. On the one hand, some adaptations may have run into difficulties. On the other hand, users or operators may want to know why the system evolved in a certain direction. Users may just want to know why the system is showing a given behaviour or has made a decision as the behaviour may be surprising or not expected. We argue that answering emerging questions related to situations like these requires storing execution trace models in a way that allows for travelling back and forth in time, qualifying the decision making against available evidence. In this paper, we propose temporal graph databases as a useful representation for trace models to support self-explanation, interactive diagnosis or forensic analysis. We define a generic meta-model for structuring execution traces of SAS, and show how a sequence of traces can be turned into a temporal graph model. We present a first version of a query language for these temporal graphs through a case study, and outline the potential applications for forensic analysis (after the system has finished in a potentially abnormal way), self-explanation, and interactive diagnosis at runtime.

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Original languageEnglish
Pages (from-to)46-55
Number of pages10
JournalCEUR Workshop Proceedings
Volume2245
Publication statusPublished - 18 Nov 2018
Event2018 MODELS Workshops: ModComp, MRT, OCL, FlexMDE, EXE, COMMitMDE, MDETools, GEMOC, MORSE, MDE4IoT, MDEbug, MoDeVVa, ME, MULTI, HuFaMo, AMMoRe, PAINS, MODELS-WS 2018 - Copenhagen, Denmark
Duration: 14 Oct 201819 Oct 2018

Bibliographic note

© 2018 The Authors. 'Reflecting on the Past and the Present with Temporal Graph-based Models'. Antonio Garcia-Dominguez, Nelly Bencomo, Luis Hernan Garcia Paucar. CEUR Workshop Proceedings 2245, pp. 46-55

    Keywords

  • Runtime models, Self-adaptation, Self-explanation, Temporal Graph Models

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