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A Distributed Approach to LARS Stream Reasoning (System paper)

Published online by Cambridge University Press:  20 September 2019

THOMAS EITER ,
PAUL OGRIS Open the ORCID record for PAUL OGRIS [Opens in a new window]  and
KONSTANTIN SCHEKOTIHIN Open the ORCID record for KONSTANTIN SCHEKOTIHIN [Opens in a new window]
THOMAS EITER
Affiliation:
Technische Universität Wien, Institut für Logic and Computation, KBS Group (e-mail: eiter@kr.tuwien.ac.at)
PAUL OGRIS
Affiliation:
Alpen-Adria-Universität, Klagenfurt, Austria (e-mail: konstantin.schekotihin@aau.at, paul.ogris@aau.at)
KONSTANTIN SCHEKOTIHIN
Affiliation:
Alpen-Adria-Universität, Klagenfurt, Austria (e-mail: konstantin.schekotihin@aau.at, paul.ogris@aau.at)
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Abstract

Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in order to allocate vast computing resources provided by clouds. In this paper we propose a distributed approach to stream reasoning that can efficiently split computations among different solvers communicating their results over data streams. Moreover, in order to increase the throughput of the distributed system, we suggest an interval-based semantics for the LARS language, which enables significant reductions of network traffic. Performed evaluations indicate that the distributed stream reasoning significantly outperforms existing stand-alone LARS solvers when the complexity of decision problems and the rate of incoming data are increasing.

Keywords

Stream reasoningdistributed systemsAnswer Set Programming
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 974 - 989
Copyright
© Cambridge University Press 2019 

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