Complex event processing over uncertain events: Techniques, challenges, and future directions

Akila, V.; Govindasamy, V.; Sandosh, S.

doi:10.1109/iccpeic.2016.7557198

Cited by 12 publications

(16 citation statements)

References 55 publications

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“…Dealing with all possible uncertainty cases in CEP in a single method seems improbable, so that each related work focuses on its particular restrictions. Consequently, the literature offers a wide variety of approaches, not only technical but also from a conceptual perspective [ 8 , 15 , 36 ]. In this section, we initially present the first models proposed in the literature to deal with uncertainty in event processing.…”

Section: Related Workmentioning

confidence: 99%

“…The common and widely used techniques for analyzing event flows from sensors, devices, and systems present in IoT is CEP [ 15 ]. By providing a structure based on a set of rules, CEP enables to extract information about patterns of relationships between simple events (i.e., primitive event) and derived complex events [ 16 ] (also called “situation” [ 17 ]).…”

Section: Introductionmentioning

confidence: 99%

“…According to Akila et al. [ 15 ], uncertainty under primitive events significantly degrades the precision of results in terms of false positives and false negatives. Consequently, CEP does not meet expectations of achieving objectives and decision-making capabilities in mission-critical applications under uncertainty conditions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Introductionmentioning

confidence: 99%

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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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show abstract

“…By monitoring user preferences and claims, companies can formalize the decision-making workflow based on a complex streaming analysis problem for better strategic planning of future events (Lanza-Cruz, Berlanga, and Aramburu 2018). By monitoring business-related events within an enterprise network or manufacturing process, companies can improve the efficiency and agility of the manufacturing processes (Estruch and Álvaro 2012), trace the origin of a network threat (Ren and Wang 2019), identify emerging trends for business transactions (Akila, Govindasamy, and Sandosh 2016), and predict the micro-location patterns of software firms (Kinne and Resch 2018). These detected events can further enable real-time, automatic and systematic reactions based on practical reasoning for business intelligence.…”

Section: Business Intelligencementioning

confidence: 99%

Spatiotemporal event detection: a review

Bambacus

Cervone

et al. 2020

International Journal of Digital Earth

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The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

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“…CEP is an event processing method that infers patterns or events by combining data from multiple sources in real time or near real time. 1 The CEP aims to create knowledge from meaningful events and produce responses within an acceptable turnaround time. 2 poral operators and may have better performance results within a short period of time.…”

Section: Introductionmentioning

confidence: 99%

Provenance aware run‐time verification of things for self‐healing Internet of Things applications

Aktaş

Astekin

2017

Concurrency and Computation

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Due to copyright restrictions, the access to the full text of this article is only available via subscription.We propose a run-time verification mechanism of things for self-healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. To identify faulty running behavior of things, we utilize a complex event processing technique by applying rule-based pattern detection on the events generated real time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, and bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed mechanism, we developed prototype applications using different event processing platforms. We test the prototype implementations for performance and scalability under increasing message rates. The results are promising because the processing overhead of the proposed verification mechanism is negligible.TÜBİTA

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Complex event processing over uncertain events: Techniques, challenges, and future directions

Cited by 12 publications

References 55 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

Spatiotemporal event detection: a review

Provenance aware run‐time verification of things for self‐healing Internet of Things applications

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