Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
ACM Computing Surveys (CSUR)
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Computers in Industry - Special issue: Process/workflow mining
Mining exact models of concurrent workflows
Computers in Industry - Special issue: Process/workflow mining
Discovery of temporal patterns from process instances
Computers in Industry - Special issue: Process/workflow mining
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Mining and Reasoning on Workflows
IEEE Transactions on Knowledge and Data Engineering
Process mining and verification of properties: an approach based on temporal logic
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
Knowledge discovery in inspection reports of marine structures
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In the after-assembly block manufacturing process in the shipbuilding industry, domain experts or industrial managers have the following questions regarding the first step in terms of reducing the overhead transportation cost due to irregularities not defined in a process design: ''What tasks are bottlenecks?'' and ''How long do the blocks remain waiting in stockyards?'' We provide the answers to these two questions. In the process mining framework, we propose a method automatically extracting the most frequent task flows from transport usage histories. Considering characteristics of our application, we use a clustering technique to identify heterogeneous groups of process instances, and then derive a process model independently by group. Process models extracted from real-world transportation logs, are verified by domain experts and labelled based on their interpretations. Consequently, we conceptualize the ''standard process'' from one global process model. Moreover, local models derived from groups of process instances reflect unknown context regarding characteristics of blocks. Our proposed method can provide conceptualized process models and process (or waiting in stockyards) times as a performance indicator. Providing reasonable answers to their questions, it helps domain experts better understand and manage the actual process. With the extension of the conventional methodology for our application problem, the main contributions of this research are that our proposed approach provides insight into the after-assembly block manufacturing process, and describes the first step for reducing transportation costs.