Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorMalysiak Mrozek, Bozena
dc.contributor.authorRyba, Bartlomiej
dc.contributor.authorMoleda, Marek
dc.contributor.authorHung, Che Lun
dc.contributor.authorPedrycz, Witold
dc.contributor.authorDing, Weiping
dc.contributor.authorMrozek, Dariusz
dc.date.accessioned2025-04-18T10:26:07Z
dc.date.available2025-04-18T10:26:07Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine. © 1993-2012 IEEE.
dc.identifier.citationMałysiak-Mrozek, B., Ryba, B., Molęda, M., Hung, C. L., Pedrycz, W., Ding, W., & Mrozek, D. (2024). Interpreting Industrial IoT Data Streams through Fuzzy Querying with Hysteretic Fuzzy Sets on Apache Kafka. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3409585
dc.identifier.endpage4684
dc.identifier.issn10636706
dc.identifier.issnhttp://dx.doi.org/10.1109/TFUZZ.2024.3409585
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85195375540
dc.identifier.scopusqualityQ1
dc.identifier.startpage4671
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7065
dc.identifier.volume32
dc.identifier.wosWOS:001291157800010
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectApache Kafka
dc.subjectBig Data
dc.subjectData Stream
dc.subjectFuzzy Sets
dc.subjectInternet of Things (IoT)
dc.subjectQuerying
dc.titleInterpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka
dc.typeArticle

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