Abstract

RDF Stream Processing (RSP) has been proposed as a way of bridging the gap between the Complex Event Processing (CEP) paradigm and the Semantic Web standards. Uncertainty has been recognized as a critical aspect in CEP, but it has received little attention within the context of RSP. In this paper, we investigate the impact of different RSP optimization strategies for uncertainty management. The paper describes (1) an extension of the RSP-QL⋆ data model to capture bind expressions, filter expressions, and uncertainty functions; (2) optimization techniques related to lazy variables and caching of uncertainty functions, and a heuristic for reordering uncertainty filters in query plans; and (3) an evaluation of these strategies in a prototype implementation. The results show that using a lazy variable mechanism for uncertainty functions can improve query execution performance by orders of magnitude while introducing negligible overhead. The results also show that caching uncertainty function results can improve performance under most conditions, but that maintaining this cache can potentially add overhead to the overall query execution process. Finally, the effect of the proposed heuristic on query execution performance was shown to depend on multiple factors, including the selectivity of uncertainty filters, the size of intermediate results, and the cost associated with the evaluation of the uncertainty functions.

Highlights

  • RDF Stream Processing (RSP) is based on existing Semantic Web standards but extends traditional approaches to support continuous processing of streaming RDF data

  • We evaluated the impact of explicitly managing different uncertainty types in RSP, which showed a need for research on query optimization strategies to improve uncertainty management efficiency [11]

  • Caching ensures that no uncertainty function will have to be evaluated more than once for the same input, but an increase in the number of join partners can still lead to a high number of cache look-ups that can be detrimental to query execution performance

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Summary

Introduction

RDF Stream Processing (RSP) is based on existing Semantic Web standards but extends traditional approaches to support continuous processing of streaming RDF data. While several RSP systems have been inspired by data stream management systems [1,2,3], RSP has been proposed as a candidate for bringing together the Complex Event Processing (CEP) paradigm and the Semantic Web standards [4,5,6,7] in order to target information integration and stream reasoning. The main contributions of this paper are (1) an extension of the RSP-QL data model we proposed in [11], to capture the syntax and semantics of uncertainty functions along with filter and bind expressions, (2) two technical optimization strategies for increasing query execution performance, and a heuristic to support reordering of uncertainty filters, and (3) an evaluation of these strategies in a prototype implementation.

Preliminaries
Syntax and Semantics of RSP-QL*
Syntax of Uncertainty Functions
Syntax of RSP-QL*
Semantics of RSP-QL*
Query Optimization
Evaluation
Experiment Setup
Results
Follow-up Experiment
Conclusions and Future Work
Full Text

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