Semantic Complex Event Processing for Decision Support

Keskisärkkä, Robin

doi:10.1007/978-3-319-11915-1_35

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…VALCRI was used as a basis for the use-case and experiment described in Paper II [70], and as a reference in several other publications [19,61,62,63,64,65,66].…”

Section: ] Provide Methods and Implemented Components For Accurate And Timely Detection Of Highly Significant But Potentially Infrequentmentioning

confidence: 99%

Complex Event Processing under Uncertainty in RDF Stream Processing

Keskisärkkä

2021

Linköping Studies in Science and Technology. Dissertations

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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 interchange 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 media, 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 implementation and a set of optimization strategies is introduced with a goal of reducing the impact of uncertainty on query execution performance. The results show that the proposed approach to representing statement-level metadata reduces required data transfer bandwidth and that it can improve query execution performance compared with using RDF reification. The optimization strategies produce improved query execution performance overall, but the impact of the heuristic depends on multiple factors, including the selectivity of filters, join cardinalities, and the cost of evaluating uncertainty functions.

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“…VALCRI was used as a basis for the use-case and experiment described in Paper II [70], and as a reference in several other publications [19,61,62,63,64,65,66].…”

Section: ] Provide Methods and Implemented Components For Accurate And Timely Detection Of Highly Significant But Potentially Infrequentmentioning

confidence: 99%

Complex Event Processing under Uncertainty in RDF Stream Processing

Keskisärkkä

2021

Linköping Studies in Science and Technology. Dissertations

Self Cite

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“…They have compared CQELS, C‐SPARQL, and JTALIS, and results show that these three engines have not yet been optimized for scalable processing . Keskisärkkä and Blomqvist surveyed existing CEP engines for analyzing streams of social media data for crisis management. They have provided a qualitative evaluation of four engines, CQELS, C‐SPARQL, ETALIS, and INTANS, as the more representative engines used by the research community.…”

Section: Related Workmentioning

confidence: 99%

Performance evaluation of linked stream data processing engines for situational awareness applications

Lachhab

Bakhouya

Ouladsine

et al. 2017

Concurrency and Computation

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Summary The easy deployment of wireless sensors allows the development of context‐aware applications that could react to the environment changes and users preferences. For example, data extracted from mobile phones, IoT devices, and embedded computers in buses and taxis could be processed in real time to understand city dynamics. However, gathering and real‐time processing of relevant data are still a challenging task. For instance, the high volume of streaming data requires real‐time processing in order to generate immediate mitigation actions. Complex‐event processing (CEP) techniques and predictive analytics have been recently proposed for analyzing streaming data in order to generate fast insights and then take suitable actions according to the situations' changes. These techniques could be also used to predict future situations and react to them properly before happening. The work presented in this paper focuses mainly on the performance evaluation of three CEP engines, CQELS, C‐SPARQL, and ETALIS, that are widely used by researchers for linked stream data processing. Experiments have been conducted using two existing benchmarks, CityBench and SP2Bench. Several performance metrics have been evaluated to assess on their ability to process high streaming data using complex queries. Results are reported to show the efficiency and scalability of these CEP engines for both social‐based data (SP2Bench) and physical‐based data (CityBench). Reported results show that ETALIS outperforms CQELS and C‐SPARQL in terms of throughput and memory utilization. ETALIS was integrated as a use case scenario for occupancy and air quality comfort in energy‐efficient buildings. Results show the usefulness of CEP for real‐time data processing and reasoning.

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The Semantic Complex Event Processing Based on Metagraph Approach

Gapanyuk

2019

Advances in Intelligent Systems and Computing

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Semantic Complex Event Processing for Decision Support

Cited by 7 publications

References 13 publications

Complex Event Processing under Uncertainty in RDF Stream Processing

Complex Event Processing under Uncertainty in RDF Stream Processing

Performance evaluation of linked stream data processing engines for situational awareness applications

The Semantic Complex Event Processing Based on Metagraph Approach

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