Mixed Materials
Complex Event Processing under Uncertainty in RDF Stream Processing
| Title: | Complex Event Processing under Uncertainty in RDF Stream Processing |
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| Authors: | Keskisärkkä, Robin |
| Publisher Information: | Linköpings universitet, Interaktiva och kognitiva system Linköpings universitet, Tekniska fakulteten Linköping 2021 |
| Document Type: | Mixed materials |
| Abstract: | The Semantic Web provides a framework for representing, sharing, and integrating data on the Web using a set of specifications promoted by the World Wide Web Consortium (W3C). These specifications include RDF as the model for data inter-change on the Web and languages (e.g., RDFS and OWL) for defining schemas and ontologies. While the Semantic Web has traditionally focused on static or slowly changing data, information on the Web is becoming increasingly dynamic, with sources such as Internet-of-Things devices, sensor networks, smart cities, social me-dia, and more. RDF Stream Processing (RSP) extends Semantic Web technologies to support streaming data and continuous queries and has been suggested as a candidate for bridging the gap between Complex Event Processing (CEP), which focuses on identifying meaningful events and event patterns from streaming data, and the Semantic Web standards. Systems that operate on real-world data must often deal with uncertainty, which can arise from, for example, missing information, incomplete domain knowledge, sensor noise, or linguistic vagueness. Uncertainty has received attention in both Semantic Web and CEP research, but little is known about how it can be managed in RSP and how it might impact performance. The contributions of this thesis are threefold. First, the issue of supporting a general model of CEP in RSP is addressed. A set of requirements for CEP is identified and used to define an event ontology for use in RSP. An approach is then proposed for creating a CEP framework that can scale processing beyond the limitations of a single RSP instance. Second, an extension of the RSP-QL data model is defined for representation of statement-level annotations. The data model is then used as a basis for capturing different types of uncertainty in a use case inspired by a research project in electronic healthcare. Finally, the performance impact of explicitly managing different types of uncertainty is evaluated in a prototype implementati Funding agencies: This work was partly funded by: (1) VALCRI, financed by the EuropeanUnion Seventh Framework Programme (FP7/2007–2013) underthe EC Grant Agreement No FP7-IP608142; (2) E-care@home, financedby the Swedish Knowledge Foundation; and (3) STeDS, partlyfinanced by the research organization CENIIT (project id 12.10).Revisions: 2021-10-06 The thesis was first published online. The online published version reflects the printed version. 2022-04-27 The thesis was updated with an errata list which is downloadable from here. Before this date the PDF was downloaded 207 times. |
| Index Terms: | Computer Sciences, Datavetenskap (datalogi), Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| DOI: | 10.3384.diss.diva-179481 |
| URL: | Linköping Studies in Science and Technology. Dissertations, 0345-7524 ; 2153 |
| Availability: | Open access content. Open access content info:eu-repo/semantics/openAccess |
| Note: | application/pdf English |
| Other Numbers: | UPE oai:DiVA.org:liu-179481 0000-0003-0644-4051 urn:isbn:9789179296216 doi:10.3384/diss.diva-179481 1312815742 |
| Contributing Source: | UPPSALA UNIV LIBR From OAIster®, provided by the OCLC Cooperative. |
| Accession Number: | edsoai.on1312815742 |
| Database: | OAIster |
| DOI: | 10.3384.diss.diva-179481 |
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