Process Mining for Data-Aware Service Compositions
Final Report Abstract
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 significant 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 significantly 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 specifically, 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 of control-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.
Publications
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Instance Migration Validity for Dynamic Evolution of Data-Aware Processes. IEEE Transactions on Software Engineering, 45(8), 782-801.
Song, Wei; Ma, Xiaoxing & Jacobsen, Hans-Arno
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Scientific Workflow Protocol Discovery from Public Event Logs in Clouds. IEEE Transactions on Knowledge and Data Engineering, 32(12), 2453-2466.
Song, Wei; Jacobsen, Hans-Arno & Chen, Fangfei
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An Empirical Study on Data Flow Bugs in Business Processes. IEEE Transactions on Cloud Computing, 9(1), 88-101.
Song, Wei; Zhang, Chengzhen & Jacobsen, Hans-Arno
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Dependence-Based Data-Aware Process Conformance Checking. IEEE Transactions on Services Computing, 14(3), 654-667.
Song, Wei; Jacobsen, Hans-Arno; Zhang, Chengzhen & Ma, Xiaoxing
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Self-Healing Event Logs. IEEE Transactions on Knowledge and Data Engineering, 33(6), 2750-2763.
Song, Wei; Jacobsen, Hans-Arno & Zhang, Pengcheng
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Why Do My Blockchain Transactions Fail?. Proceedings of the 2021 International Conference on Management of Data, 221-234. ACM.
Chacko, Jeeta Ann; Mayer, Ruben & Jacobsen, Hans-Arno
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Workflow Refactoring for Maximizing Concurrency and Block-Structuredness. IEEE Transactions on Services Computing, 14(4), 1224-1237.
Song, Wei; Jacobsen, Hans-Arno; Cheung, S.C.; Liu, Hongyu & Ma, Xiaoxing
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Discovering Structural Errors From Business Process Event Logs. IEEE Transactions on Knowledge and Data Engineering, 34(11), 5293-5306.
Song, Wei; Chang, Zhen; Jacobsen, Hans-Arno & Zhang, Pengcheng
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Identifying a Minimum Sequence of High-Level Changes Between Workflows. IEEE Transactions on Services Computing, 15(4), 2425-2438.
Song, Wei; Chen, Fangfei; Jacobsen, Hans-Arno & Zhang, Chengzhen
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Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines. Proceedings of the 2022 International Conference on Management of Data, 1825-1839. ACM.
Isenko, Alexander; Mayer, Ruben; Jedele, Jeffrey & Jacobsen, Hans-Arno
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How To Optimize My Blockchain? A Multi-Level Recommendation Approach. Proceedings of the ACM on Management of Data, 1(1), 1-27.
Chacko, Jeeta Ann; Mayer, Ruben & Jacobsen, Hans-Arno
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How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study. Proceedings of the VLDB Endowment, 17(6), 1214-1226.
Erben, Alexander; Mayer, Ruben & Jacobsen, Hans-Arno
