Towards self-healing in the internet of things by log analytics and process miningShow others and affiliations
2020 (English)In: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, Research Publishing Services, 2020, p. 4644-4651Conference paper, Published paper (Refereed)
Abstract [en]
The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing. © ESREL2020-PSAM15 Organizers.Published by Research Publishing, Singapore.
Place, publisher, year, edition, pages
Research Publishing Services, 2020. p. 4644-4651
Keywords [en]
Diagnostics, Event correlation, Internet of Things, Prognostics, Self-healing, Self-repair, Threat detection, Data mining, Safety engineering, Self-healing materials, Connected systems, Critical applications, Future generations, Future prospects, Heterogeneous devices, Internet of thing (IOT), Problem detection, Process mining
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-108440DOI: 10.3850/978-981-14-8593-0_5634-cdScopus ID: 2-s2.0-85110274646ISBN: 9789811485930 (print)OAI: oai:DiVA.org:lnu-108440DiVA, id: diva2:1618041
Conference
30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020, Venice 1-5 November 2020
2021-12-082021-12-082024-08-28Bibliographically approved