Managing Measurement and Occurrence Uncertainty in Complex Event Processing Systems

Moreno, Nathalie; Bertoa, Manuel F.; Burgueño, Loli; Vallecillo, Antonio

doi:10.1109/access.2019.2923953

Cited by 11 publications

(9 citation statements)

References 44 publications

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“…The scope of this study is considered a relevant research problem in the literature [ 8 , 11 , 12 , 13 , 18 , 19 , 46 ]. Various theoretical methods were explored such as probabilistic approaches, Bayesian, Fuzzy, among others [ 6 , 19 , 40 ].…”

Section: Related Workmentioning

confidence: 99%

Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

Bezerra

Teles

Coutinho

et al. 2021

Sensors

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The Internet of Things (IoT) has emerged from the proliferation of mobile devices and objects connected, resulting in the acquisition of periodic event flows from different devices and sensors. However, such sensors and devices can be faulty or affected by failures, have poor calibration, and produce inaccurate data and uncertain event flows in IoT applications. A prominent technique for analyzing event flows is Complex Event Processing (CEP). Uncertainty in CEP is usually observed in primitive events (i.e., sensor readings) and rules that derive complex events (i.e., high-level situations). In this paper, we investigate the identification and treatment of uncertainty in CEP-based IoT applications. We propose the DST-CEP, an approach that uses the Dempster–Shafer Theory to treat uncertainties. By using this theory, our solution can combine unreliable sensor data in conflicting situations and detect correct results. DST-CEP has an architectural model for treating uncertainty in events and its propagation to processing rules. We describe a case study using the proposed approach in a multi-sensor fire outbreak detection system. We submit our solution to experiments with a real sensor dataset, and evaluate it using well-known performance metrics. The solution achieves promising results regarding Accuracy, Precision, Recall, F-measure, and ROC Curve, even when combining conflicting sensor readings. DST-CEP demonstrated to be suitable and flexible to deal with uncertainty.

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Section: Related Workmentioning

confidence: 99%

Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

Bezerra

Teles

Coutinho

et al. 2021

Sensors

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“…Moreno et al [89] instead define uncertainty as a quality or state that involves imperfect and/or unknown information. They refer to two main types of uncertainty: measurement uncertainty and occurrence uncertainty.…”

Section: Definitions Of Uncertaintymentioning

confidence: 99%

“…The importance of temporal constraints when detecting event patterns in CEP suggests that it might be meaningful to treat temporal uncertainty as a distinct uncertainty type, although it could also potentially be viewed as a special kind of attribute uncertainty. It is worth noting that very few CEP systems implement any support for event time uncertainty [89,131].…”

Section: Types Of Uncertaintymentioning

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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“…To do that, we have used a probabilistic approach, instead of employing fuzzy logic or possibility theory. We have worked on the representation of measurement uncertainty in software models [19,20] and in CEP systems [21]. Our solution, presented in the form of a library that can be added to existing CEP engines, focuses on measurement information (also known as epistemic uncertainty), the confidence we have in the data we handle, and the rules determining the behavior of the digital avatars (aleatory uncertainty).…”

Section: Complex Event Processing With Uncertainty the Definition And Detection Of Situations Of Interest From The Analysis Of Low-level mentioning

confidence: 99%

“…However, we are able to incorporate data uncertainty to the rule by using the uncertain data types (UBooleans, UReals, UIntegers, etc.) defined in the libraries we have implemented and presented in previous works [19,21]. Thus, Code 3 presents the same high pressure detection rule but adding uncertainty to the data received from the blood pressure monitor.…”

Section: Complex Event Processing With Uncertainty the Definition And Detection Of Situations Of Interest From The Analysis Of Low-level mentioning

confidence: 99%

Digital Avatars: Promoting Independent Living for Older Adults

Bertoa

Moreno

Pérez-Vereda

et al. 2020

Wireless Communications and Mobile Computing

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Population ageing, together with the desire to maintain an autonomous lifestyle, poses today’s societies with a challenge that technological advances can help considerably to cope with. The widespread use of smartphones and their increasing computing power and storage capacity make them the ideal tool to achieve this goal. In this paper, we present Digital Avatars, a software framework adapted to the needs of older adults who wish to preserve their lifestyle, but who require assistance through technology. Building on previous work on the People as a Service model, Digital Avatars takes advantage of a smartphone’s capabilities and services to collect information about the people who own them. To do this, it applies Complex Event Processing techniques extended with uncertainty to infer the habits, preferences, and needs of the device owner to build with them an enhanced virtual profile of the user. These virtual profiles are the mechanism for monitoring the quality of life of older adults: analyzing their patterns of activity, reminding them of medication schedules, or detecting risky situations that generate alerts to relatives, caregivers, or the community health system.

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Managing Measurement and Occurrence Uncertainty in Complex Event Processing Systems

Cited by 11 publications

References 44 publications

Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

Complex Event Processing under Uncertainty in RDF Stream Processing

Digital Avatars: Promoting Independent Living for Older Adults

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