Leveraging path information to generate predictions for parallel business processes
| Autor: | Merve Unuvar, Geetika T. Lakshmanan, Yurdaer N. Doganata |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Business process
Computer science Process (engineering) business.industry Decision tree 02 engineering and technology Business process modeling Machine learning computer.software_genre Outcome (game theory) Human-Computer Interaction Set (abstract data type) Artificial Intelligence Hardware and Architecture 020204 information systems Path (graph theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer Software Information Systems TRACE (psycholinguistics) |
| Zdroj: | Knowledge and Information Systems. 47:433-461 |
| ISSN: | 0219-3116 0219-1377 |
| DOI: | 10.1007/s10115-015-0842-7 |
| Popis: | In semi-structured processes, the set of activities that need to be performed, their order and whether additional steps are required are determined by human judgment. There is a growing demand for operational support of such processes during runtime particularly in the form of predictions about the likelihood of future tasks. We address the problem of making predictions for a running instance of a semi-structured process that contains parallel execution paths where the execution path taken by a process instance influences its outcome. In particular, we consider five different models for how to represent an execution trace as a path attribute for training a prediction model. We provide a methodology to determine whether parallel paths are independent, and whether it is worthwhile to model execution paths as independent based on a comparison of the information gain obtained by dependent and independent path representations. We tested our methodology by simulating a marketing campaign as a business process model and selected decision trees as the prediction model. In the evaluation, we compare the complexity and prediction accuracy of a prediction model trained with five different models. |
| Databáze: | OpenAIRE |
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