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Identifying Batch Processing Features in Workflows
Abstract
The employment of batch processing in workflow is to model and enact the batch processing logic for multiple cases of a workflow in order to optimize business processes execution dynamically. Our previous work has preliminarily investigated the model and its implementation. However, it does not figure out precisely which activity and how a/multiple workflow activity(s) can gain execution efficiency from batch processing. Inspired by workflow mining and functional dependency inference, this chapter proposes a method for mining batch processing patterns in workflows from process dataflow logs. We first introduce a new concept, batch dependency, which is a specific type of functional dependency in database. The theoretical foundation of batch dependency as well as its mining algorithms is analyzed and investigated. Based on batch dependency and its discovery technique, the activities meriting batch processing and their batch processing features are identified. With the batch processing features discovered, the batch processing areas in workflow are recognized then. Finally, an experiment is demonstrated to show the effectiveness of our method.
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