AI Bibliography

WIKINDX Resources

Reinkemeyer, L. (2022). Status and future of process mining: From process discovery to process execution. In Process Mining Handbook (pp. 405–415). Springer. 
Resource type: Book Article
BibTeX citation key: Reinkemeyer2022
View all bibliographic details
Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General, Mathematics
Subcategories: Analytics, Behavioral analytics, Big data, Chaos theory, Decision making, Deep learning, Forecasting, Informatics, Machine learning, Markov models, Systems theory
Creators: Reinkemeyer
Publisher: Springer
Collection: Process Mining Handbook
During the last two decades Process Mining has seen a rapid global adoption: first in academics and then in corporate business. It has evolved into a foundational technology, allowing users to discover actual process flows with unprecedented transparency, speed, and detail. In a business environment Process Mining has no purpose of its own, but companies leverage it to identify process inefficiencies, improve process execution and ultimately drive value. Process discovery and transparency does not provide immediate business value, but requires specific use cases combined with human intelligence to identify and deploy levers for process improvement. In this article we argue that the future focus and evolution of Process Mining shall not focus on lateral expansion - i.e. with further processes and discoveries - but vertically by enhancing the depth of added value for business users with artificial intelligence, proactive and predictive enablement and other levers which boost process execution. In essence, focus should be on deploying smarter technologies for driving business value in process areas where Process Mining has shown impact.
WIKINDX 6.7.0 | Total resources: 1582 | Username: -- | Bibliography: WIKINDX Master Bibliography | Style: American Psychological Association (APA)