Capturing and Querying Uncertainty in RDF Stream Processing
2020 (English)In: 22nd International Conference on Knowledge Engineering and Knowledge Management, EKAW 2020, Springer Science and Business Media Deutschland GmbH , 2020, p. 37-53Conference paper, Published paper (Refereed)
Abstract [en]
RDF Stream Processing (RSP) has been proposed as a candidate for bringing together the Complex Event Processing (CEP) paradigm and the Semantic Web standards. In this paper, we investigate the impact of explicitly representing and processing uncertainty in RSP for the use in CEP. Additionally, we provide a representation for capturing the relevant notions of uncertainty in the RSP-QL data model and describe query functions that can operate on this representation. The impact evaluation is based on a use-case within electronic healthcare, where we compare the query execution overhead of different uncertainty options in a prototype implementation. The experiments show that the influence on query execution performance varies greatly, but that uncertainty can have noticeable impact on query execution performance. On the other hand, the overhead grows linearly with respect to the stream rate for all uncertainty options in the evaluation, and the observed performance is sufficient for many use-cases. Extending the representation and operations to support more uncertainty options and investigating different query optimization strategies to reduce the impact on execution performance remain important areas for future research.
Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2020. p. 37-53
Keywords [en]
CEP, RSP, RSP-QL, Uncertainty, Knowledge management, Complex event processing (CEP), Electronic healthcare, Execution performance, Impact evaluation, Prototype implementations, Query optimization strategies, Semantic web standards, Stream processing, Semantic Web
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:ri:diva-50990DOI: 10.1007/978-3-030-61244-3_3Scopus ID: 2-s2.0-85096497579ISBN: 9783030612436 (print)OAI: oai:DiVA.org:ri-50990DiVA, id: diva2:1508432
Conference
16 September 2020 through 20 September 2020
2020-12-102020-12-102023-12-05Bibliographically approved