%0 Conference Proceedings
%T How People Really (Like To) Work
%+ Eindhoven University of Technology [Eindhoven] (TU/e)
%A Aalst, Wil, van Der
%Z Part 3: Keynotes
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 5th International Conference on Human-Centred Software Engineering (HCSE)
%C Paderborn, Germany
%Y Stefan Sauer
%Y Cristian Bogdan
%Y Peter Forbrig
%Y Regina Bernhaupt
%Y Marco Winckler
%I Springer
%3 Human-Centered Software Engineering
%V LNCS-8742
%P 317-321
%8 2014-09-16
%D 2014
%R 10.1007/978-3-662-44811-3_25
%Z Computer Science [cs]Conference papers
%X Software forms an integral part of the most complex artifacts built by humans. Communication, production, distribution, healthcare, transportation, banking, education, entertainment, government, and trade all increasingly rely on systems driven by software. Such systems may be used in ways not anticipated at design time as the context in which they operate is constantly changing and humans may interact with them an unpredictable manner. However, at the same time, we are able to collect unprecedented collections of event data describing what people and organizations are actually doing. Recent developments in process mining make it possible to analyze such event data, thereby focusing on behavior rather than correlations and simplistic performance indicators. For example, event logs can be used to automatically learn end-to-end process models. Next to the automated discovery of the real underlying process, there are process mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards “better” processes. Process mining reveals how people really work and often reveals what they would really like to do. Event-based analysis may reveal workarounds and remarkable differences between people and organizations. This keynote paper highlights current research on comparative process mining. One can compare event data with normative process models and see where people deviate. Some of these deviations may be positive and one can learn from them. Other deviations may reveal inefficiencies, design flaws, or even fraudulent behavior. One can also use process cubes to compare different systems or groups of people. Through slicing, dicing, rolling-up, and drilling-down we can view event data from different angles and produce process mining results that can be compared.
%G English
%Z TC 13
%Z WG 13.2
%2 https://inria.hal.science/hal-01405093/document
%2 https://inria.hal.science/hal-01405093/file/978-3-662-44811-3_25_Chapter.pdf
%L hal-01405093
%U https://inria.hal.science/hal-01405093
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-LNCS-8742
%~ IFIP-TC13
%~ IFIP-HCSE
%~ IFIP-WG13-2