乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T21:53:17Z","timestamp":1724968397439},"reference-count":57,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T00:00:00Z","timestamp":1557446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["wires.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["WIREs Data Min & Knowl"],"published-print":{"date-parts":[[2019,11]]},"abstract":"Abstract<\/jats:title>Sequential pattern mining (SPM) is a well\u2010studied theme in data mining, in which one aims to discover common sequences of item sets in a large corpus of temporal itemset data. Due to the sequential nature of data streams, supporting SPM in streaming environments is commonly studied in the area of data stream mining as well. On the other hand, stream\u2010based process discovery (PD), originating from the field of process mining, focusses on learning process models on the basis of online event data. In particular, the main goal of the models discovered is to describe the underlying generating process in an end\u2010to\u2010end fashion. As both SPM and PD use data that are comparable in nature, that is, both involve time\u2010stamped instances, one expects that techniques from the SPM domain are (partly) transferable to the PD domain. However, thus far, little work has been done in the intersection of the two fields. In this focus article, we therefore study the possible application of SPM techniques in the context of PD. We provide an overview of the two fields, covering their commonalities and differences, highlight the challenges of applying them, and, present an outlook and several avenues for future work.<\/jats:p>This article is categorized under:\nAlgorithmic Development\u2009>\u2009Spatial and Temporal Data Mining<\/jats:p><\/jats:list-item>\nFundamental Concepts of Data and Knowledge\u2009>\u2009Key Design Issues in Data Mining<\/jats:p><\/jats:list-item>\nFundamental Concepts of Data and Knowledge\u2009>\u2009Big Data Mining<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p>","DOI":"10.1002\/widm.1315","type":"journal-article","created":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T07:59:59Z","timestamp":1557475199000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["On the application of sequential pattern mining primitives to process discovery: Overview, outlook and opportunity identification"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4027-4351","authenticated-orcid":false,"given":"Marwan","family":"Hassani","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven The Netherlands"}]},{"given":"Sebastiaan J.","family":"van Zelst","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Applied Information Technology, FIT Sankt Augustin Germany"},{"name":"Process and Data Science Group RWTH Aachen University Aachen Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0955-6940","authenticated-orcid":false,"given":"Wil M. 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