Enhancing the scalability of expressive stream reasoning via input-driven parallelization.
Le Pham, Thu, Ali, Muhammad IntizarORCID: 0000-0002-0674-2131 and Mileo, AlessandraORCID: 0000-0002-6614-6462
(2019)
Enhancing the scalability of expressive stream reasoning via input-driven parallelization.
Semantic Web, 10
(3).
pp. 457-474.
ISSN 1570-0844
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-of-the-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.
Item Type:
Article (Published)
Refereed:
Yes
Uncontrolled Keywords:
Semantic Web, stream reasoning; non-monotonic reasoning; Answer Set Programming; parallel reasoning, data partitioning; dependency graph