Lecture notes in business information processing, 2023
Overprocessing is a source of waste that occurs when unnecessary work is performed in a process. ... more Overprocessing is a source of waste that occurs when unnecessary work is performed in a process. Overprocessing is often found in application-to-approval processes since a rejected application does not add value, and thus, work that leads to the rejection constitutes overprocessing. Analyzing how the knockout checks are executed can help analysts to identify opportunities to reduce overprocessing waste and time. This paper proposes an interpretable process mining approach for discovering improvement opportunities in the knockout checks and recommending redesigns to address them. Experiments on synthetic and real-life event logs show that the approach successfully identifies improvement opportunities while attaining a performance comparable to black-box approaches. Moreover, by leveraging interpretable machine learning techniques, our approach provides further insights on knockout check executions, explaining to analysts the logic behind the suggested redesigns. The approach is implemented as a software tool and its applicability is demonstrated on a real-life process.
Business process simulation is a well-known approach to estimate the impact of changes to a proce... more Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures-a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches.
Data and generative models for research article <em>Discovering Generative Models from Even... more Data and generative models for research article <em>Discovering Generative Models from Event Logs: Data-driven Simulation vs Deep Learning</em>. This record contains the complete and partitioned event logs, deep learning generative models (*.h5 format), simulation models for the BIMP simulator (*.bpmn format), and all the raw and summarized results of the paper. The source code of the project can be found at https://github.com/AdaptiveBProcess/DDSvsDL
Este proyecto busca exponer cómo la ISC puede aplicarse a la solución de problemas complejos de l... more Este proyecto busca exponer cómo la ISC puede aplicarse a la solución de problemas complejos de las organizaciones, mediante el uso de técnicas de la ISC en la solución de un problema complejo común a las instituciones prestadoras de servicios de salud: el conocimiento de la dinámica del servicio de aseo para mejorar su planeación.
Business process simulation is a versatile technique to estimate the performance of a process und... more Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-p...
A generative model is a statistical model that is able to generate new data instances from previo... more A generative model is a statistical model that is able to generate new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two families of generative process simulation models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation technique with multiple deep learning techniques, which construct models are capable of generating execution traces with timestamped events. The study sheds light into the relative strengths of both approaches and raises the prospect of developing hybrid approaches that combine these strengths.
Business process simulation is a versatile technique for analyzing business processes from a quan... more Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to discover multitasking in business process execution logs and to generate a simulation model that takes into account the discovered multitasking behavior. The key idea is to adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original processing times. The proposed approach is evaluated using a real-life dataset and synthetic datasets with different levels of multitasking. The results show that, in the presence of multitasking, the approach improves the accuracy of simulation models discovered from execution logs.
Business process simulation is a well-known approach to estimate the impact of changes to a proce... more Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods combine automated process discovery and enhancement techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to capture the temporal dynamics of real-life processes. In contrast, parallel work has shown that generative Deep Learning (DL) models are able to accurately capture such temporal dynamics. The drawback of these latter models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) ...
Business process simulation is a widespread approach for quantitative analysis of business proces... more Business process simulation is a widespread approach for quantitative analysis of business processes. However, the creation of accurate business process simulation models is a laborious and error-prone task, due to the numerous parameters that need to be carefully tuned. Additionally, the accuracy of a simulation model is inherently limited by the accuracy of the process model that is used as a starting point. This paper presents Simod: A tool to automatically generate simulation models from event logs. Simod uses an automated process discovery technique to extract a process model from an event log and then enhances this model with simulation parameters extracted via a combination of trace alignment, replay, and curve-fitting techniques. The tool incorporates a Bayesian hyperparameter optimization technique to fine-tune the accuracy of the resulting simulation model.
Business process simulation is a versatile technique for analyzing business processes from a quan... more Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to di...
Deep learning techniques have recently found applications in the field of predictive business pro... more Deep learning techniques have recently found applications in the field of predictive business process monitoring. These techniques allow us to predict, among other things, what will be the next events in a case, when will they occur, and which resources will trigger them. They also allow us to generate entire execution traces of a business process, or even entire event logs, which opens up the possibility of using such models for process simulation. This paper addresses the question of how to use deep learning techniques to train accurate models of business process behavior from event logs. The paper proposes an approach to train recurrent neural networks with Long-Short-Term Memory (LSTM) architecture in order to predict sequences of next events, their timestamp, and their associated resource pools. An experimental evaluation on real-life event logs shows that the proposed approach outperforms previously proposed LSTM architectures targeted at this problem.
Business process simulation is a versatile technique to estimate the performance of a process und... more Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.
2016 IEEE 11th International Conference on Global Software Engineering (ICGSE), 2016
This paper presents MONO, a tool to support interdisciplinary and geographically dispersed teams.... more This paper presents MONO, a tool to support interdisciplinary and geographically dispersed teams. MONO supports the definition, execution, and monitoring of collaborative processes to allow development engineers to interact with musicians, designers, and producers, to create digital contents such as video games or animated short films. MONO supports geographically distributed teams, involved in digital content software development projects, by means of dynamic and configurable executable processes, and coordination and monitoring tools. In this work, we describe our experience developing MONO and the results obtained in our initial validation.
HighlightsA maturity model for collaboration on digital content projects is proposed.Projects are... more HighlightsA maturity model for collaboration on digital content projects is proposed.Projects are intensive in resources, knowledge, and time-cost-quality constraints.The model defines capabilities on processes, people, technology, and information.The software MONO was created to automate inter/intra organizational capabilities.These capabilities should be gradually adopted to improve projects efficiency. The digital content industry requires the integration of specialized information and communications technology (ICT) capabilities to support collaborative work for planning and executing its business processes. In particular, this knowledge-intensive industry lacks for adequate control on product documentation, inter and intra organizational resources management, and process monitoring which is required for supporting the high demand of projects typically constrained in time, costs, and quality. This paper presents a defined maturity model named DigiCoMM to assess collaboration and interoperability capabilities that are specific to pre-production, production, and post-production processes within the digital content industry. It also presents MONO, a computer-supported collaborative work (CSCW) software for supporting the incremental transition of companies through the different levels of the maturity model. MONO was developed in the context of the DAVID research project (Strategic Programme for the Research and Development of the Colombian Animation and Video Games Industry), during the period of 2012-2015. This model and software were used to assess and support the collaborative capabilities of several animation and video game companies in Colombia.
A generative model is a statistical model capable of generating new data instances from previousl... more A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.
Lecture notes in business information processing, 2023
Overprocessing is a source of waste that occurs when unnecessary work is performed in a process. ... more Overprocessing is a source of waste that occurs when unnecessary work is performed in a process. Overprocessing is often found in application-to-approval processes since a rejected application does not add value, and thus, work that leads to the rejection constitutes overprocessing. Analyzing how the knockout checks are executed can help analysts to identify opportunities to reduce overprocessing waste and time. This paper proposes an interpretable process mining approach for discovering improvement opportunities in the knockout checks and recommending redesigns to address them. Experiments on synthetic and real-life event logs show that the approach successfully identifies improvement opportunities while attaining a performance comparable to black-box approaches. Moreover, by leveraging interpretable machine learning techniques, our approach provides further insights on knockout check executions, explaining to analysts the logic behind the suggested redesigns. The approach is implemented as a software tool and its applicability is demonstrated on a real-life process.
Business process simulation is a well-known approach to estimate the impact of changes to a proce... more Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures-a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches.
Data and generative models for research article <em>Discovering Generative Models from Even... more Data and generative models for research article <em>Discovering Generative Models from Event Logs: Data-driven Simulation vs Deep Learning</em>. This record contains the complete and partitioned event logs, deep learning generative models (*.h5 format), simulation models for the BIMP simulator (*.bpmn format), and all the raw and summarized results of the paper. The source code of the project can be found at https://github.com/AdaptiveBProcess/DDSvsDL
Este proyecto busca exponer cómo la ISC puede aplicarse a la solución de problemas complejos de l... more Este proyecto busca exponer cómo la ISC puede aplicarse a la solución de problemas complejos de las organizaciones, mediante el uso de técnicas de la ISC en la solución de un problema complejo común a las instituciones prestadoras de servicios de salud: el conocimiento de la dinámica del servicio de aseo para mejorar su planeación.
Business process simulation is a versatile technique to estimate the performance of a process und... more Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-p...
A generative model is a statistical model that is able to generate new data instances from previo... more A generative model is a statistical model that is able to generate new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two families of generative process simulation models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation technique with multiple deep learning techniques, which construct models are capable of generating execution traces with timestamped events. The study sheds light into the relative strengths of both approaches and raises the prospect of developing hybrid approaches that combine these strengths.
Business process simulation is a versatile technique for analyzing business processes from a quan... more Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to discover multitasking in business process execution logs and to generate a simulation model that takes into account the discovered multitasking behavior. The key idea is to adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original processing times. The proposed approach is evaluated using a real-life dataset and synthetic datasets with different levels of multitasking. The results show that, in the presence of multitasking, the approach improves the accuracy of simulation models discovered from execution logs.
Business process simulation is a well-known approach to estimate the impact of changes to a proce... more Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods combine automated process discovery and enhancement techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to capture the temporal dynamics of real-life processes. In contrast, parallel work has shown that generative Deep Learning (DL) models are able to accurately capture such temporal dynamics. The drawback of these latter models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) ...
Business process simulation is a widespread approach for quantitative analysis of business proces... more Business process simulation is a widespread approach for quantitative analysis of business processes. However, the creation of accurate business process simulation models is a laborious and error-prone task, due to the numerous parameters that need to be carefully tuned. Additionally, the accuracy of a simulation model is inherently limited by the accuracy of the process model that is used as a starting point. This paper presents Simod: A tool to automatically generate simulation models from event logs. Simod uses an automated process discovery technique to extract a process model from an event log and then enhances this model with simulation parameters extracted via a combination of trace alignment, replay, and curve-fitting techniques. The tool incorporates a Bayesian hyperparameter optimization technique to fine-tune the accuracy of the resulting simulation model.
Business process simulation is a versatile technique for analyzing business processes from a quan... more Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to di...
Deep learning techniques have recently found applications in the field of predictive business pro... more Deep learning techniques have recently found applications in the field of predictive business process monitoring. These techniques allow us to predict, among other things, what will be the next events in a case, when will they occur, and which resources will trigger them. They also allow us to generate entire execution traces of a business process, or even entire event logs, which opens up the possibility of using such models for process simulation. This paper addresses the question of how to use deep learning techniques to train accurate models of business process behavior from event logs. The paper proposes an approach to train recurrent neural networks with Long-Short-Term Memory (LSTM) architecture in order to predict sequences of next events, their timestamp, and their associated resource pools. An experimental evaluation on real-life event logs shows that the proposed approach outperforms previously proposed LSTM architectures targeted at this problem.
Business process simulation is a versatile technique to estimate the performance of a process und... more Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.
2016 IEEE 11th International Conference on Global Software Engineering (ICGSE), 2016
This paper presents MONO, a tool to support interdisciplinary and geographically dispersed teams.... more This paper presents MONO, a tool to support interdisciplinary and geographically dispersed teams. MONO supports the definition, execution, and monitoring of collaborative processes to allow development engineers to interact with musicians, designers, and producers, to create digital contents such as video games or animated short films. MONO supports geographically distributed teams, involved in digital content software development projects, by means of dynamic and configurable executable processes, and coordination and monitoring tools. In this work, we describe our experience developing MONO and the results obtained in our initial validation.
HighlightsA maturity model for collaboration on digital content projects is proposed.Projects are... more HighlightsA maturity model for collaboration on digital content projects is proposed.Projects are intensive in resources, knowledge, and time-cost-quality constraints.The model defines capabilities on processes, people, technology, and information.The software MONO was created to automate inter/intra organizational capabilities.These capabilities should be gradually adopted to improve projects efficiency. The digital content industry requires the integration of specialized information and communications technology (ICT) capabilities to support collaborative work for planning and executing its business processes. In particular, this knowledge-intensive industry lacks for adequate control on product documentation, inter and intra organizational resources management, and process monitoring which is required for supporting the high demand of projects typically constrained in time, costs, and quality. This paper presents a defined maturity model named DigiCoMM to assess collaboration and interoperability capabilities that are specific to pre-production, production, and post-production processes within the digital content industry. It also presents MONO, a computer-supported collaborative work (CSCW) software for supporting the incremental transition of companies through the different levels of the maturity model. MONO was developed in the context of the DAVID research project (Strategic Programme for the Research and Development of the Colombian Animation and Video Games Industry), during the period of 2012-2015. This model and software were used to assess and support the collaborative capabilities of several animation and video game companies in Colombia.
A generative model is a statistical model capable of generating new data instances from previousl... more A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.
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