Streaming Process Discovery and Conformance Checking

Burattin, Andrea

doi:10.1007/978-3-319-63962-8_103-1

Cited by 4 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taxonomy of the different approaches to solve the different stream process mining problems. For each technique, corresponding general steps are sketched [10].…”

Section: Taxonomy Of Approachesmentioning

confidence: 99%

Streaming Process Mining

Burattin

2022

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

Streaming process mining refers to the set of techniques and tools which have the goal of processing a stream of data (as opposed to a finite event log). The goal of these techniques, similarly to their corresponding counterparts described in the previous chapters, is to extract relevant information concerning the running processes. This chapter presents an overview of the problems related to the processing of streams, as well as a categorization of the existing solutions. Details about control-flow discovery and conformance checking techniques are also presented together with a brief overview of the state of the art.

show abstract

“…Taxonomy of the different approaches to solve the different stream process mining problems. For each technique, corresponding general steps are sketched [10].…”

Section: Taxonomy Of Approachesmentioning

confidence: 99%

Streaming Process Mining

Burattin

2022

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

show abstract

Towards IoT-driven Process Event Log Generation for Conformance Checking in Smart Factories

Seiger

Zerbato

Burattin

et al. 2020

2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)

Self Cite

View full text Add to dashboard Cite

The Internet of Things (IoT) enables software-based access to vast amounts of data streams from sensors measuring physical and virtual properties of smart devices and their surroundings. While sophisticated means for the control and data analysis of single IoT devices exist, a more process-oriented view of IoT systems is often missing. Such a lack of process awareness hinders the development of process-based systems on top of IoT environments and the application of process mining techniques for process analysis and optimization in IoT. We propose a framework for the stepwise correlation and composition of raw IoT sensor streams with events and activities on a process level based on Complex Event Processing (CEP). From this correlation we derive refined process event logs-possibly with ambiguities-that can be used for process analysis at runtime (i. e., online). We discuss the framework using examples from a smart factory.

show abstract