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ICPM 2027: International Conference on Process Mining

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ICPM
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投稿締切日:
2026-09-04 残り 15 日
通知日:
2026-11-04
開催日:
2027-02-08
開催地:
Rende, Italy
開催回数:
ICORE: B   閲覧: 14792   フォロー: 2   参加: 1

論文募集

ICPM 2027 (International Conference on Process Mining) is a ICORE B conference held in Rende, Italy on 2027-02-08. The paper submission deadline is 2026-09-04. Acceptance notifications are sent on 2026-11-04.

The International Conference on Process Mining (ICPM) is the premier international venue for researchers and professionals working in the field of process mining. It promotes a vibrant exchange of ideas and expertise through scientific presentations, interactive industry sessions, live challenges, practical tutorials, and expert-led panels. ICPM addresses the full spectrum of process mining research and practice, spanning theoretical progress, algorithmic research, real-world impact and applications, and interdisciplinary links with areas such as machine learning, data science, and operations management. Supported by the IEEE Task Force on Process Mining, ICPM stands at the forefront of innovation in process mining research and practice. Process mining reshapes the way organizations explore, analyze, and optimize their operational processes by deriving insights from transactional data stored in information systems. This evidence- driven approach allows organizations to improve their processes across critical dimensions, including efficiency, quality, compliance, and risk management. By moving beyond traditional assumption-based methods toward data-driven decision-making, process mining opens new avenues for advancing business process management. Topics for Research Papers ICPM 2027 welcomes rigorous contributions spanning both technical and empirical process mining research, aiming to advance the state of the art and expand existing knowledge in the field. Submissions should explicitly articulate their original contributions and novelty. Submissions are encouraged to provide an evaluation appropriate to their contribution. This may involve theoretical demonstrations and examples (for conceptual and theoretical work), quantitative or comparative analysis (for technical work), or evidence of practical relevance such as case studies or newly collected data (for empirical work). Studies utilizing novel, unpublished datasets are particularly welcomed. Research using existing datasets must emphasize the novelty of the analysis or its unprecedented results. To enhance transparency, reproducibility, and replicability, authors are strongly encouraged to provide supplementary resources, such as used datasets, publicly accessible implementations, and experimental packages for empirical studies. For more details, please consult the Open Science Principles section below. Selected accepted papers may be invited for an extended and revised publication in a special issue of the flagship journal Process Science (https://link.springer.com/journal/44311). ICPM 2027 welcomes submissions across a broad spectrum of topics, including but not limited to: Technical Research in Process Mining ● Novel process mining methods and techniques for: ○ Event log building and preprocessing ○ Process discovery ○ Conformance checking ○ Performance analysis ○ Comparative process mining ○ Predictive and prescriptive process monitoring ○ Action-oriented process mining ● Formal foundations of process mining ● Conceptual models related to process mining ● Process mining visualizations Empirical Research in Process Mining ● Case studies and applications ● Experiments involving process mining methods and techniques ● Human-in-the-loop studies ● Survey- and interview-based studies
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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
Journal of Process Control3.9Elsevier0959-1524
BJournal of Software: Evolution and Process1.8Wiley2047-7481
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203

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