Computing clouds have become the platform of choice for the deployment and execution of scientific workflows. Due to the uncertainty and unpredictability of scientific exploration, the execution plan for a scientific workflow may vary from the definition. It is therefore of great significance to be able to discover actual workflows from execution histories (event logs) to reproduce experimental results and to establish provenance. However, most existing process mining techniques focus on discovering control flow-oriented business processes in a centralized environment, and thus, they are mostly inapplicable to the discovery of data flow-oriented, unstructured scientific workflows in distributed cloud environments. In this paper, we present Scientific Workflow Mining as a Service (SWMaaS) to support both intra-cloud and inter-cloud scientific workflow mining. The approach is implemented as a ProM plug-in and is evaluated on event logs derived from real-world scientific workflows. Through experimental results, we demonstrate the effectiveness and efficiency of our approach.

The aligning of event logs with process models is of great significance for process mining to enable conformance checking, process enhancement, performance analysis, and trace repairing. Since process models are increasingly complex and event logs may deviate from process models by exhibiting redundant, missing, and dislocated events, it is challenging to determine the optimal alignment for each event sequence in the log, as this problem is NP-hard. Existing approaches utilize the cost-based A* algorithm to address this problem. However, scalability is often not considered, which is especially important when dealing with industrial-sized problems. In this paper, by taking advantage of the structural and behavioral features of process models, we present an efficient approach which leverages effective heuristics and trace replaying to significantly reduce the overall search space for seeking the optimal alignment. We employ real-world business processes and their traces to evaluate the proposed approach. Experimental results demonstrate that our approach works well in most cases, and that it outperforms the state-of-the-art approach by up to 5 orders of magnitude in runtime efficiency.

While event logs generated by business processes play an increasingly significant role in business analysis, the quality of data remains a serious problem. Automatic recovery of dirty event logs is desirable and thus receives more attention. However, existing methods only focus on missing event recovery, or fall short of efficiency. To this end, we present Effa, a ProM plugin, to automatically recover event logs in the light of process specifications. Based on advanced heuristics including process decomposition and trace replaying to search the minimum recovery, Effa achieves a balance between repairing accuracy and efficiency.

Event logs are of paramount significance for process mining and complex event processing. Yet, the quality of event logs remains a serious problem. Missing events of logs are usually caused by omitting manual recording, system failures, and hybrid storage of executions of different processes. It has been proved that the problem of minimum recovery based on a priori process specification is NP-hard. State-of-the-art approach is still lacking in efficiency because of the large search space. To address this issue, in this paper, we leverage the technique of process decomposition and present heuristics to efficiently prune the unqualified sub-processes that fail to generate the minimum recovery. We employ real-world processes and their incomplete sequences to evaluate our heuristic approach. The experimental results demonstrate that our approach achieves high accuracy as the state-of-the-art approach does, but it is more efficient.