{"id":91,"date":"2019-05-03T12:00:51","date_gmt":"2019-05-03T10:00:51","guid":{"rendered":"https:\/\/blog.rwth-aachen.de\/pads\/?p=91"},"modified":"2019-04-26T11:47:04","modified_gmt":"2019-04-26T09:47:04","slug":"uncertanty-in-process-mining-finding-bounds-for-conformance-cost","status":"publish","type":"post","link":"https:\/\/blog.rwth-aachen.de\/pads\/2019\/05\/03\/uncertanty-in-process-mining-finding-bounds-for-conformance-cost\/","title":{"rendered":"Uncertanty in process mining: finding bounds for conformance cost"},"content":{"rendered":"\n

This post is by Marco Pegoraro<\/a>, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.<\/em><\/p>\n\n\n\n

Let’s consider the analysis of a specific class of event logs: the logs that contain uncertain event data<\/em>. Uncertain events are recordings of executions of specific activities in a process which are enclosed with an indication of uncertainty in the event attributes. Specifically, let’s consider the case where the attributes of an event are not recorded as a precise value but as a range or a set of alternatives. In the case of the common control-flow attributes of an event log, the case id and activity are represented by a set of alternatives, and the timestamp as a range.<\/p>\n\n\n\n

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An example of uncertain trace.<\/figcaption><\/figure><\/div>\n\n\n\n

We can think of this uncertain trace as a set of “classic” traces obtained by every possible choices of the values for the attributes; this is called the realization set<\/em>.<\/p>\n\n\n\n

In the paper “Mining Uncertain Event Data in Process Mining<\/em>“, published on the first International Conference on Process Mining (ICPM<\/a>), we laid down a taxonomy of possible kinds of uncertainty; we also explore an example of application, i.e. the computation of upper and lower bound for conformance cost of an uncertain trace against a reference process model. This is obtained by modeling a Petri net able to reproduce the behavior of an uncertain trace; this enables to compute the conformance exploiting a model-to-model comparison.<\/p>\n","protected":false},"excerpt":{"rendered":"

This post is by Marco Pegoraro, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries. Let’s consider the analysis […]<\/p>\n","protected":false},"author":1679,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"c2c_always_allow_admin_comments":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-91","post","type-post","status-publish","format-standard","hentry","category-allgemein"],"_links":{"self":[{"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/posts\/91","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/users\/1679"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/comments?post=91"}],"version-history":[{"count":1,"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/posts\/91\/revisions"}],"predecessor-version":[{"id":93,"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/posts\/91\/revisions\/93"}],"wp:attachment":[{"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/media?parent=91"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/categories?post=91"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.rwth-aachen.de\/pads\/wp-json\/wp\/v2\/tags?post=91"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}