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Using log analytics...
Using log analytics and process mining to enable self-healing in the Internet of Things
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- Singh, P. (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM)
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- Saman Azari, Mehdi (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),DISA;DISA-SIG
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- Vitale, F. (författare)
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy
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- Flammini, Francesco, Senior Lecturer, 1978- (författare)
- Linnéuniversitetet,Mälardalens universitet,Innovation och produktrealisering,Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden,Institutionen för datavetenskap och medieteknik (DM),Mälardalen University, Sweden
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- Mazzocca, N. (författare)
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy
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- Caporuscio, Mauro, 1975- (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),DISA;DISA-SIG
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- Thornadtsson, J. (författare)
- Information, Sigma Technology, Gothenburg, Sweden
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(creator_code:org_t)
- 2022-05-17
- 2022
- Engelska.
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Ingår i: Environment Systems and Decisions. - : Springer. - 2194-5403 .- 2194-5411. ; 42:2, s. 234-250
- Relaterad länk:
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https://doi.org/10.1...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The Internet of Things (IoT) is rapidly developing in diverse and critical applications such as environmental sensing and industrial control systems. IoT devices can be very heterogeneous in terms of hardware and software architectures, communication protocols, and/or manufacturers. Therefore, when those devices are connected together to build a complex system, detecting and fixing any anomalies can be very challenging. In this paper, we explore a relatively novel technique known as Process Mining, which—in combination with log-file analytics and machine learning—can support early diagnosis, prognosis, and subsequent automated repair to improve the resilience of IoT devices within possibly complex cyber-physical systems. Issues addressed in this paper include generation of consistent Event Logs and definition of a roadmap toward effective Process Discovery and Conformance Checking to support Self-Healing in IoT.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Anomaly detection
- Cyber-physical systems
- Data driven
- Resilience
- Self-diagnostics
- Self-repair
- analytical method
- detection method
- Internet
- machine learning
- software
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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