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Machine learning for optimizing disposition and planning of vehicles with near real-time IoT events at scale

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dc.contributor.authorDaemi-Ahwazi, Anusch
dc.contributor.authorRost, Daniel
dc.contributor.editorReussner, Ralf H.
dc.contributor.editorKoziolek, Anne
dc.contributor.editorHeinrich, Robert
dc.date.accessioned2021-01-27T13:34:11Z
dc.date.available2021-01-27T13:34:11Z
dc.date.issued2021
dc.description.abstractCargo vehicles today are equipped with power saving IoT devices measuring various aspects of the vehicle and cargo itself. The real-time stream of IoT events from the vehicles are sending large amounts of data each day, which needs to be correlated with each other and existing data sources to generate business value. The algorithmic challenges for discussion are the handling of noisy data and fast correlation of the sensor data as well as software engineering challenges to ensure the system(s) are highly performant and maintainable over the next decades.en
dc.identifier.doi10.18420/inf2020_67
dc.identifier.isbn978-3-88579-701-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34778
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2020
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-307
dc.subjectMachine Learning
dc.subjectGraph Theory
dc.subjectIoT
dc.subjectSoftware Engineering
dc.titleMachine learning for optimizing disposition and planning of vehicles with near real-time IoT events at scaleen
gi.citation.endPage767
gi.citation.startPage765
gi.conference.date28. September - 2. Oktober 2020
gi.conference.locationKarlsruhe
gi.conference.sessiontitleGraph theory & ML with real-time IoT data

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P307 - INFORMATIK 2020 - Back to the Future