Human-in-the-loop anomaly detection in industrial data streams

2023
book section
other documents
dc.abstract.enThe detection of anomalies in an industrial setting remains an important and open challenge for most manufacturing companies. The potential benefits from the utilization of an anomaly detection system are substantial, as deviations from normal operating conditions can cause downtimes, quailty issues or safety hazards. The main requirements for an anomaly detection system include the selection of the machine learning model applicable to streaming data, providing the explanations of the model’s decision and participation of human operator in the learning process of the model. We have proposed the anomaly detection system, which addresses the above challenges and is applicable in industrial environment.pl
dc.affiliationWydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanejpl
dc.contributor.authorJakubowski, Jakubpl
dc.contributor.authorBobek, Szymon - 428058 pl
dc.contributor.authorNalepa, Grzegorz - 200414 pl
dc.contributor.editorGena, Cristinapl
dc.contributor.editorde Russis, Luigipl
dc.contributor.editorSpano, Davidepl
dc.contributor.editorLanzilotti, Rosapl
dc.contributor.editordi Mascio, Taniapl
dc.contributor.editorPrandi, Catiapl
dc.contributor.editorAndolina, Salvatorepl
dc.date.accessioned2023-10-10T12:13:40Z
dc.date.available2023-10-10T12:13:40Z
dc.date.issued2023pl
dc.description.additionalPoster pokonferencyjny. Article ID: 46pl
dc.description.physical[1-2]pl
dc.description.publication0,2pl
dc.description.sponsorshipsourceNarodowe Centrum Naukipl
dc.identifier.doi10.1145/3605390.3610830pl
dc.identifier.isbn979-8-4007-0806-0pl
dc.identifier.project2020/02/Y/ST6/00070pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/320842
dc.languageengpl
dc.language.containerengpl
dc.pubinfoNew York : The Association for Computing Machinerypl
dc.rightsDodaję tylko opis bibliograficzny*
dc.rights.licencebez licencji
dc.rights.uri*
dc.sourceinfoliczba autorów 216; liczba stron 416; liczba arkuszy wydawniczych 34,6;pl
dc.subject.endata streamspl
dc.subject.enanomaly detectionpl
dc.subject.enexplainable artificial intelligencepl
dc.subtypeOtherDocumentspl
dc.titleHuman-in-the-loop anomaly detection in industrial data streamspl
dc.title.containerProceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter, CHItaly 2023, 20-22 September 2023, Torino, Italypl
dc.typeBookSectionpl
dspace.entity.typePublication
dc.abstract.enpl
The detection of anomalies in an industrial setting remains an important and open challenge for most manufacturing companies. The potential benefits from the utilization of an anomaly detection system are substantial, as deviations from normal operating conditions can cause downtimes, quailty issues or safety hazards. The main requirements for an anomaly detection system include the selection of the machine learning model applicable to streaming data, providing the explanations of the model’s decision and participation of human operator in the learning process of the model. We have proposed the anomaly detection system, which addresses the above challenges and is applicable in industrial environment.
dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej
dc.contributor.authorpl
Jakubowski, Jakub
dc.contributor.authorpl
Bobek, Szymon - 428058
dc.contributor.authorpl
Nalepa, Grzegorz - 200414
dc.contributor.editorpl
Gena, Cristina
dc.contributor.editorpl
de Russis, Luigi
dc.contributor.editorpl
Spano, Davide
dc.contributor.editorpl
Lanzilotti, Rosa
dc.contributor.editorpl
di Mascio, Tania
dc.contributor.editorpl
Prandi, Catia
dc.contributor.editorpl
Andolina, Salvatore
dc.date.accessioned
2023-10-10T12:13:40Z
dc.date.available
2023-10-10T12:13:40Z
dc.date.issuedpl
2023
dc.description.additionalpl
Poster pokonferencyjny. Article ID: 46
dc.description.physicalpl
[1-2]
dc.description.publicationpl
0,2
dc.description.sponsorshipsourcepl
Narodowe Centrum Nauki
dc.identifier.doipl
10.1145/3605390.3610830
dc.identifier.isbnpl
979-8-4007-0806-0
dc.identifier.projectpl
2020/02/Y/ST6/00070
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/320842
dc.languagepl
eng
dc.language.containerpl
eng
dc.pubinfopl
New York : The Association for Computing Machinery
dc.rights*
Dodaję tylko opis bibliograficzny
dc.rights.licence
bez licencji
dc.rights.uri*
dc.sourceinfopl
liczba autorów 216; liczba stron 416; liczba arkuszy wydawniczych 34,6;
dc.subject.enpl
data streams
dc.subject.enpl
anomaly detection
dc.subject.enpl
explainable artificial intelligence
dc.subtypepl
OtherDocuments
dc.titlepl
Human-in-the-loop anomaly detection in industrial data streams
dc.title.containerpl
Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter, CHItaly 2023, 20-22 September 2023, Torino, Italy
dc.typepl
BookSection
dspace.entity.type
Publication
Affiliations

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