Abstract

The project “Semantic Technologies for Situation Awareness” was concerned with detecting certain critical situations from data obtained by observing a complex hard- and software system, in order to trigger actions that allow this system to save energy. The general idea was to formalize situations as ontology-mediated queries, but in order to express the relevant situations, both the employed ontology language and the query language had to be extended. In this paper we sketch the general approach and then concentrate on reporting the formal results obtained for reasoning in these extensions, but do not describe the application that triggered these extensions in detail.

Highlights

  • We have investigated conjunctive queries (CQs) answering over ontologies that use concrete domains in [7], which is the foundation for reasoning with probabilistic concrete domains, studied in phase II and for a practical reasoning procedure for query answering in DL-Lite augmented with a concrete domain over the real numbers [1]

  • In cooperation with the HAEC project headed by Christel Baier, we explored how the design-time probabilistic model checking (PMC) techniques employed in that project can be combined with our run-time monitoring approach

  • We have investigated the complexity of reasoning in light-weight DLs [24, 25] under different types of repair semantics, taking rigidity of concepts and roles into account, and were able to show that the complexity often does not increase compared to the case of classical temporal reasoning

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Summary

Introduction

The main challenges addressed in this project were how to represent context information relevant for enhancing energy efficiency in a formally well-founded way, how to integrate context information obtained from different sources into a coherent semantic view, and how to reason about this view in order to decide on an appropriate action, like moving running processes from an underutilized compute node to another one with remaining capacity, to allow shutting down the former one To address these challenges, we employed ontologies expressed in appropriate decidable,. The raw data, such as subsymbolic sensor information, information on the hardware provided by the operating system, or information from other sources, are preprocessed, aggregated and cleaned (by the database system) and used to populate the fact base, which contains information expressed in terms of the ontology This pre-processing is realized by database queries. For work by others we refer the reader to the descriptions of related work in the cited papers

Extending DL Reasoning for Basic Context Recognition
Temporalizing OBDA‐Based Context Recognition
Admitting Vagueness for Ontology‐Based
Incorporating Concrete Data Values
Addressing Advanced Challenges for Context Recognition
Incorporating Probabilities
Resilience Against Inconsistency
Linking Context Recognition and Probabilistic Model Checking
Other Cooperation
Conclusions
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