THE 17TH LEARNING AND INTELLIGENT OPTIMIZATION CONFERENCE (LION17 SCOPE)
DATA-DRIVEN OPTIMIZATION WITH BUSINESS PROCESS MINING
June 4 – 8, 2023 |NICE, FRANCE
About Special Session 2:
Process mining, positioned at the interface between ‘Process science’ and ‘Data science’, combines event data with process models and intends to gain insights, identify bottlenecks, predict problems, and optimize organizational processes. Process mining, already being used for high-volume processes in large organizations, will soon become the ‘new normal’ for smaller organizations and processes with fewer cases as well.
Despite a huge surge in research endeavors in Process Discovery, Conformance Checking, and Model Enhancement, positioning them as three verticals of Process Mining, there are a number of research challenges that need to be overcome to realize the vision of data driven optimization of business processes. The optimization paradigm in the process mining context is being explored at the following levels:
1. When it comes to creating process models, event logs generated by process-oriented information systems are treated as a critical resource. Conformance checking can be formulated as an optimization problem with the model and log repair. Thus, conformance checking corresponds to solving optimization problems that grow exponentially in the size of the model and the length of traces in the event log
2. Optimization metaheuristics have also been widely applied in the context of automated process discovery, with the goal of gradual discovery and advancement of process models to achieve a tradeoff between accuracy and simplicity. The most notorious of these approaches are those based on evolutionary (genetic) algorithms. However, several other metaheuristics have been researched, such as Imperialist competition algorithms, swarm particle optimization, and simulated glow in this context
3. Data ingestion from diverse source systems is supported by AI, which allows to identify and customize structured and unstructured data from various sources. Thus, various optimization techniques can be used to improve the performance of the data transformation discovery techniques in the context of the synthesis of routine specifications.
With rapidly growing applications, this special session invites original, unpublished research contributions that demonstrate current findings in the area of application of data science and optimization techniques for process mining, with special reference to algorithms for process discovery, conformance checking, and process model enhancement.
Organizer : Om Prakash Vyas (Indian Institute of Information Technology, Allahabad , India) and Jerome Geyer-Klingeberg (Celonis, Munich, Germany)
Theme of the conference will be including but not limited to the following areas:
- Process Mining, Process Optimization
- Process Discovery, Process Conformance, Process Enhancement
- Event Data Analytics for Process optimization, Anomaly detection in Event logs
- Online analysis and optimization of the business process
- ‘Process Mining Applications’ in Healthcare, IoT, Cyber-security, AI, Finance, etc.
We invite submissions presenting new and original research on all topics related to and relevant to the conference. All papers will be reviewed in a double-blind process, and accepted papers will be presented at the conference. Submitted papers can have a maximum length of 15 pages (including references) and should use the LNCS template. Papers must be submitted as a PDF in English. Papers accepted into the LION17 proceedings will be published in Lecture Notes in Computer Science (LNCS).
Abstract submission (For Full Paper): January 23, 2023
Full paper submission: February 1, 2023
Presentation only’ Abstracts: March 1, 2023
Author notification: March 8, 2023
Conference pre-proceedings: May 1, 2023