suraj pandey | LOVELY PROFESSIONAL UNIVERSITY (original) (raw)

Papers by suraj pandey

Business process simulation is an effective approach to many organizations to understand the prog... more Business process simulation is an effective approach to many organizations to understand the progress of processes over a period of time and predict future activities, which can then be given as a feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efÀciency. In particular, organisations require to study the process and event Áows, recognize their patterns, and forecast the total time it would take for a workÁow to complete. This paper proposes the architecture for business process simulation and describes its prototype implementation. It lists several prediction techniques that have been implemented as part of the prototype system. The paper also describes an artiÀcial neural network model that could be used for predicting the completion time of business processes that are constrained by the availability of resources. 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-0-7695-4691-9/12 $26.00

Business process prediction technologies are being increasingly used by organisations to provide ... more Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to predict the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete, in order to meet the Service Level Agreements (SLAs) signed with the customers. In this paper, we focus on the prediction models that could be used for forecasting time to completion of business processes by analysing historical event logs. First, we propose a service oriented architecture that provides a test-bed for carrying out predictions on business processes. Second, we propose a Hidden Markov Model (HMM) based prediction technique that produces a model based on event logs, and compare it against existing prediction models. Finally, we describe an implementation of the system, where we simulate the execution of a business process and obtain predictions using both the proposed and existing prediction techniques.

Business process simulation is an effective approach to many organizations to understand the prog... more Business process simulation is an effective approach to many organizations to understand the progress of processes over a period of time and predict future activities, which can then be given as a feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efÀciency. In particular, organisations require to study the process and event Áows, recognize their patterns, and forecast the total time it would take for a workÁow to complete. This paper proposes the architecture for business process simulation and describes its prototype implementation. It lists several prediction techniques that have been implemented as part of the prototype system. The paper also describes an artiÀcial neural network model that could be used for predicting the completion time of business processes that are constrained by the availability of resources. 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-0-7695-4691-9/12 $26.00

Business process prediction technologies are being increasingly used by organisations to provide ... more Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to predict the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete, in order to meet the Service Level Agreements (SLAs) signed with the customers. In this paper, we focus on the prediction models that could be used for forecasting time to completion of business processes by analysing historical event logs. First, we propose a service oriented architecture that provides a test-bed for carrying out predictions on business processes. Second, we propose a Hidden Markov Model (HMM) based prediction technique that produces a model based on event logs, and compare it against existing prediction models. Finally, we describe an implementation of the system, where we simulate the execution of a business process and obtain predictions using both the proposed and existing prediction techniques.

Business process simulation is an effective approach to many organizations to understand the prog... more Business process simulation is an effective approach to many organizations to understand the progress of processes over a period of time and predict future activities, which can then be given as a feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efÀciency. In particular, organisations require to study the process and event Áows, recognize their patterns, and forecast the total time it would take for a workÁow to complete. This paper proposes the architecture for business process simulation and describes its prototype implementation. It lists several prediction techniques that have been implemented as part of the prototype system. The paper also describes an artiÀcial neural network model that could be used for predicting the completion time of business processes that are constrained by the availability of resources. 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-0-7695-4691-9/12 $26.00

Business process prediction technologies are being increasingly used by organisations to provide ... more Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to predict the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete, in order to meet the Service Level Agreements (SLAs) signed with the customers. In this paper, we focus on the prediction models that could be used for forecasting time to completion of business processes by analysing historical event logs. First, we propose a service oriented architecture that provides a test-bed for carrying out predictions on business processes. Second, we propose a Hidden Markov Model (HMM) based prediction technique that produces a model based on event logs, and compare it against existing prediction models. Finally, we describe an implementation of the system, where we simulate the execution of a business process and obtain predictions using both the proposed and existing prediction techniques.

Business process simulation is an effective approach to many organizations to understand the prog... more Business process simulation is an effective approach to many organizations to understand the progress of processes over a period of time and predict future activities, which can then be given as a feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efÀciency. In particular, organisations require to study the process and event Áows, recognize their patterns, and forecast the total time it would take for a workÁow to complete. This paper proposes the architecture for business process simulation and describes its prototype implementation. It lists several prediction techniques that have been implemented as part of the prototype system. The paper also describes an artiÀcial neural network model that could be used for predicting the completion time of business processes that are constrained by the availability of resources. 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-0-7695-4691-9/12 $26.00

Business process prediction technologies are being increasingly used by organisations to provide ... more Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to predict the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete, in order to meet the Service Level Agreements (SLAs) signed with the customers. In this paper, we focus on the prediction models that could be used for forecasting time to completion of business processes by analysing historical event logs. First, we propose a service oriented architecture that provides a test-bed for carrying out predictions on business processes. Second, we propose a Hidden Markov Model (HMM) based prediction technique that produces a model based on event logs, and compare it against existing prediction models. Finally, we describe an implementation of the system, where we simulate the execution of a business process and obtain predictions using both the proposed and existing prediction techniques.