Enhancing the scalability of expressive stream reasoning via input-driven parallelization

Abstract

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-ofthe-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and nonmonotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of a rule layer based on a fragment of Answer Set Programming (ASP). We recently expanded on this approach by designing an algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and combining the results.

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

“…Each rule has an atom corresponding to one of the relationships as its only sub-target. Both rules have the same IDB predicate at the head [34]. The parameters of a rule's header are the same as those of its sub-targets.…”

Microblog topic identification using Linked Open Data

“…Generally, electing to execute the more restrictive queries prior to others which restricts the search space is considered a good approach. Finally, generating streams of semantic topics could be facilitated with stream reasoning [124] and queried with a stream query language such as C-SPARQL [125] and C-SPRITE [126].…”

Handling Impossible Derivations During Stream Reasoning

“…Additionally, other stream reasoners either propose a custom processing model [20] or rely on (probabilistic) ASP [15,25,38] or on combinations of the two [22]. Finally, some works focus on improving the scalability [16,26,27] by distributing the computation on multiple machines or with incremental techniques [19]. Since these works support different semantics, it is challenging to compare them.…”