Process Mining for Data-Aware Service Compositions

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How to guarantee the trustworthiness and quality of service compositions in the open, dynamic, and uncontrollable environments is a key research issue in the domain of software engineering and services computing. As a signicant means to constructing service compositions and ensuring their trustworthiness, processes and the corresponding process mining techniques have received extensive attention. However, existing process mining approaches mostly focus on the control-flow analysis, failing to satisfy the technical requirements relevant to data-aware service compositions. Particularly, when the demand of evolution is urgent, the event logs of service compositions are usually incomplete, which signicantly affects the mining quality of existing techniques. To address the above problems, this project, by combining software analysis and data mining, aims to study process mining approaches based on essential event relations (i.e., dependences, independences, mutual-exclusions) in event logs. More specically, we hope to make considerable progress in the following aspects: maximizing concurrency and block-structuredness for process discovery, data-aware process conformance checking based on trace dependence graphs, as well as detection ofcontrol-flow errors from event logs. Furthermore, we will realize these approaches and techniques in some software tools and corresponding supporting platforms, in order to provide technical support for improving the trustworthiness and quality of service compositions.