Context-aware analysis of past process executions to aid resource allocation decisions

The allocation of resources to process tasks can have a significant impact on the performance (such as cost, time) of those tasks, and hence of the overall process. Past resource allocation decisions, when correlated with process execution histories annotated with quality of service (or performance) measures, can be a rich source of knowledge about the best resource allocation decisions. The optimality of resource allocation decisions is not determined by the process instance alone, but also by the context in which these instances are executed. This phenomenon turns out to be even more compelling when the resources in question are human resources. Human workers with same the organizational role and capabilities can have heterogeneous behaviors based on their operational context. In this work, we propose an approach to supporting resource allocation decisions by extracting information about the process context and process performance from past process executions. The information extracted is analyzed using exploratory data mining techniques to discover resource allocation decisions. The knowledge thus acquired can be used to guide resource allocations in new process instances. Experiments performed on synthetic and real-world execution logs demonstrate the effectiveness of the proposed approach.