@article{Mangler_DataStreamExtension_2023,
	title        = {{DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs}},
	author       = {Mangler, Juergen and Grüger, Joscha and Malburg, Lukas and Ehrendorfer, Matthias and Bertrand, Yannis and Benzin, Janik-Vasily and Rinderle-Ma, Stefanie and Serral Asensio, Estefania and Bergmann, Ralph},
	year         = 2023,
	journal      = {Future Internet},
	volume       = 15,
	number       = 3,
	doi          = {10.3390/fi15030109},
	url          = {http://www.wi2.uni-trier.de/shared/publications/2023_ManglerEtAl_DataStreamExtension.pdf},
	abstract     = {{The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.}},
	keywords     = {{Process Management, Industry 4.0, IoT data, Process Mining, XES}}
}

{"_id":"kyqHCw3SrkKjB3j8c","bibbaseid":"mangler-grger-malburg-ehrendorfer-bertrand-benzin-rinderlema-serralasensio-etal-datastreamxesextensionembeddingiotsensordataintoextensibleeventstreamlogs-2023","author_short":["Mangler, J.","Grüger, J.","Malburg, L.","Ehrendorfer, M.","Bertrand, Y.","Benzin, J.","Rinderle-Ma, S.","Serral Asensio, E.","Bergmann, R."],"bibdata":{"bibtype":"article","type":"article","title":"DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs","author":[{"propositions":[],"lastnames":["Mangler"],"firstnames":["Juergen"],"suffixes":[]},{"propositions":[],"lastnames":["Grüger"],"firstnames":["Joscha"],"suffixes":[]},{"propositions":[],"lastnames":["Malburg"],"firstnames":["Lukas"],"suffixes":[]},{"propositions":[],"lastnames":["Ehrendorfer"],"firstnames":["Matthias"],"suffixes":[]},{"propositions":[],"lastnames":["Bertrand"],"firstnames":["Yannis"],"suffixes":[]},{"propositions":[],"lastnames":["Benzin"],"firstnames":["Janik-Vasily"],"suffixes":[]},{"propositions":[],"lastnames":["Rinderle-Ma"],"firstnames":["Stefanie"],"suffixes":[]},{"propositions":[],"lastnames":["Serral","Asensio"],"firstnames":["Estefania"],"suffixes":[]},{"propositions":[],"lastnames":["Bergmann"],"firstnames":["Ralph"],"suffixes":[]}],"year":"2023","journal":"Future Internet","volume":"15","number":"3","doi":"10.3390/fi15030109","url":"http://www.wi2.uni-trier.de/shared/publications/2023_ManglerEtAl_DataStreamExtension.pdf","abstract":"The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.","keywords":"Process Management, Industry 4.0, IoT data, Process Mining, XES","bibtex":"@article{Mangler_DataStreamExtension_2023,\n\ttitle = {{DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs}},\n\tauthor = {Mangler, Juergen and Grüger, Joscha and Malburg, Lukas and Ehrendorfer, Matthias and Bertrand, Yannis and Benzin, Janik-Vasily and Rinderle-Ma, Stefanie and Serral Asensio, Estefania and Bergmann, Ralph},\n\tyear = 2023,\n\tjournal = {Future Internet},\n\tvolume = 15,\n\tnumber = 3,\n\tdoi = {10.3390/fi15030109},\n\turl = {http://www.wi2.uni-trier.de/shared/publications/2023_ManglerEtAl_DataStreamExtension.pdf},\n\tabstract = {{The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.}},\n\tkeywords = {{Process Management, Industry 4.0, IoT data, Process Mining, XES}}\n}\n\n","author_short":["Mangler, J.","Grüger, J.","Malburg, L.","Ehrendorfer, M.","Bertrand, Y.","Benzin, J.","Rinderle-Ma, S.","Serral Asensio, E.","Bergmann, R."],"key":"Mangler_DataStreamExtension_2023","id":"Mangler_DataStreamExtension_2023","bibbaseid":"mangler-grger-malburg-ehrendorfer-bertrand-benzin-rinderlema-serralasensio-etal-datastreamxesextensionembeddingiotsensordataintoextensibleeventstreamlogs-2023","role":"author","urls":{"Paper":"http://www.wi2.uni-trier.de/shared/publications/2023_ManglerEtAl_DataStreamExtension.pdf"},"keyword":["Process Management","Industry 4.0","IoT data","Process Mining","XES"],"metadata":{"authorlinks":{}},"downloads":11},"bibtype":"article","biburl":"https://web.wi2.uni-trier.de/publications/PublicationsMalburg.bib","dataSources":["nZxfXH3fRFhwWejKL","MSp3DzP4ToPojqkFy","Td7BJ334QwxWK4vLW","J3orK6zvpR7d8vDmC"],"keywords":["process management","industry 4.0","iot data","process mining","xes"],"search_terms":["datastream","xes","extension","embedding","iot","sensor","data","extensible","event","stream","logs","mangler","grüger","malburg","ehrendorfer","bertrand","benzin","rinderle-ma","serral asensio","bergmann"],"title":"DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs","year":2023,"downloads":11}