前沿科技

Using process mining algorithms for process improvement in healthcare

Fazla Rabbi a, Debapriya Banik b, Niamat Ullah Ibne Hossain a, Alexandr Sokolov a 2024-04-05

Healthcare professionals must provide their patients with the best possible service and be well-informed and expert at carrying out complex surgical procedures to fulfill this responsibility. The aim of the medical treatments is fewer complications, shorter hospital stays, and a better patient experience.

Demystifying data governance for process mining: Insights from a Delphi study

Kanika Goel a, Niels Martin b c, Arthur ter Hofstede a 2024-07-01

Data governance is recognised as a new capability for organisations to maximize the value of data. Process mining is essential for the resilient growth of businesses, making process data a strategic asset for organisations.

Trace encoding in process mining: A survey and benchmarking

Gabriel M. Tavares a, Rafael S. Oyamada b, Sylvio Barbon Junior c, Paolo Ceravolo b 2023-11-01

Encoding methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, trace clustering, etc. These methods are usually performed as preprocessing steps and are responsible for mapping complex event data information into a numerical feature space. Most papers choose existing encoding methods arbitrarily or employ a strategy based on expert domain knowledge.

Process mining software engineering practices: A case study for deployment pipelines

Ana Filipa Nogueira, Mário Zenha-Rela 2024-04-01

In mature software development organizations the ci/cd pipeline is the only route to deploy software into production. While the workflow of this process seems straightforward, the reality is different since exceptions and deviations are the norm in actual industry practice. In this context, Process Mining appears as a promising technique to uncover deviations and check compliance with standardized DevOps processes, and highlight bottlenecks and potential improvement areas.

Process mining and data mining applications in the domain of chronic diseases: A systematic review

Kaile Chen a b, Farhad Abtahi a b c, Juan-Jesus Carrero d, Carlos Fernandez-Llatas a e, Fernando Seoane a c f g 2023-10-01

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities.

Leveraging machine learning for automatic topic discovery and forecasting of process mining research: A literature review

Gyunam Park a, Minsu Cho b, Jiyoon Lee b 2024-04-01

Process mining is a relatively new discipline that focuses on gaining process-centric knowledge from event logs collected by enterprise systems. From an academic standpoint, there has been a constant effort to develop various techniques to automatically discover process models, analyze the compliance of real-life processes to the process models, predict operational frictions, and recommend possible actions to mitigate emerging risks.

Using process mining algorithms for process improvement in healthcare

Fazla Rabbi a, Debapriya Banik b, Niamat Ullah Ibne Hossain a, Alexandr Sokolov a 2024-04-05

Healthcare professionals must provide their patients with the best possible service and be well-informed and expert at carrying out complex surgical procedures to fulfill this responsibility. The aim of the medical treatments is fewer complications, shorter hospital stays, and a better patient experience.

Demystifying data governance for process mining: Insights from a Delphi study

Kanika Goel a, Niels Martin b c, Arthur ter Hofstede a 2024-07-01

Data governance is recognised as a new capability for organisations to maximize the value of data. Process mining is essential for the resilient growth of businesses, making process data a strategic asset for organisations.

Trace encoding in process mining: A survey and benchmarking

Gabriel M. Tavares a, Rafael S. Oyamada b, Sylvio Barbon Junior c, Paolo Ceravolo b 2023-11-01

Encoding methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, trace clustering, etc. These methods are usually performed as preprocessing steps and are responsible for mapping complex event data information into a numerical feature space. Most papers choose existing encoding methods arbitrarily or employ a strategy based on expert domain knowledge.

Process mining software engineering practices: A case study for deployment pipelines

Ana Filipa Nogueira, Mário Zenha-Rela 2024-04-01

In mature software development organizations the ci/cd pipeline is the only route to deploy software into production. While the workflow of this process seems straightforward, the reality is different since exceptions and deviations are the norm in actual industry practice. In this context, Process Mining appears as a promising technique to uncover deviations and check compliance with standardized DevOps processes, and highlight bottlenecks and potential improvement areas.

Process mining and data mining applications in the domain of chronic diseases: A systematic review

Kaile Chen a b, Farhad Abtahi a b c, Juan-Jesus Carrero d, Carlos Fernandez-Llatas a e, Fernando Seoane a c f g 2023-10-01

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities.

Leveraging machine learning for automatic topic discovery and forecasting of process mining research: A literature review

Gyunam Park a, Minsu Cho b, Jiyoon Lee b 2024-04-01

Process mining is a relatively new discipline that focuses on gaining process-centric knowledge from event logs collected by enterprise systems. From an academic standpoint, there has been a constant effort to develop various techniques to automatically discover process models, analyze the compliance of real-life processes to the process models, predict operational frictions, and recommend possible actions to mitigate emerging risks.

Using process mining algorithms for process improvement in healthcare

Fazla Rabbi a, Debapriya Banik b, Niamat Ullah Ibne Hossain a, Alexandr Sokolov a 2024-04-05

Healthcare professionals must provide their patients with the best possible service and be well-informed and expert at carrying out complex surgical procedures to fulfill this responsibility. The aim of the medical treatments is fewer complications, shorter hospital stays, and a better patient experience.

Demystifying data governance for process mining: Insights from a Delphi study

Kanika Goel a, Niels Martin b c, Arthur ter Hofstede a 2024-07-01

Data governance is recognised as a new capability for organisations to maximize the value of data. Process mining is essential for the resilient growth of businesses, making process data a strategic asset for organisations.

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