A test-bed for the evaluation of business process prediction techniques (original) (raw)
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Prediction of Business Process Execution Time
Lecture notes in networks and systems 715, 2022
The use of immense amounts of data on the execution of applications based on business processes can make it possible, thanks to Process Mining, to detect trends. Indeed, human intelligence in decisionmaking is enriched by Machine Learning in order to avoid bottlenecks, improve efficiency and highlight potential process improvements. In this research article, we present a method (BPETPM) for predictive monitoring of business processes. This method allows to predict the execution time of a business process according to the path followed by the process instance. It predicts whether a process instance will run in time or late. We follow the CRISP-DM approach, known in Data Science, to carry out our method. The input data for learning is extracted from the event logs saving the execution traces of the workflow engine of a BPMS. We start by cleaning data, adding additional attributes, and encoding categorical variables. Then, at the modelling level, we test six classification algorithms : KNN, SVM(kernel=linear), SVM(kernel=rbf), Decision Tree, Random Forest and Logestic Regression. Then, using the BPETPM method, we create an intelligent process management system (iBPMS4PET). This system is applied to a process for managing incoming mail in the mutual health sector.
A Generic Model for End State Prediction of Business Processes Towards Target Compliance
Lecture Notes in Computer Science, 2019
The prime concern for a business organization is to supply quality services to the customers without any delay or interruption so to establish a good reputation among the customer's and competitors. Ontime delivery of a customers order not only builds trust in the business organization but is also cost effective. Therefore, there is a need is to monitor complex business processes though automated systems which should be capable during execution to predict delay in processes so as to provide a better customer experience. This online problem has led us to develop an automated solution using machine learning algorithms so as to predict possible delay in business processes. The core characteristic of the proposed system is the extraction of generic process event log, graphical and sequence features, using the log generated by the process as it executes up to a given point in time where a prediction need to be made (referred to here as cutoff time); in an executing process this would generally be current time. These generic features are then used with Support Vector Machines, Logistic Regression, Naive Bayes and Decision trees to predict the data into on-time or delayed processes. The experimental results are presented based on real business processes evaluated using various metric performance measures such as accuracy, precision, sensitivity, specificity, F-measure and AUC for prediction as to whether the order will complete on-time when it has already been executing for a given period.
ACM Transactions on Intelligent Systems and Technology
Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity, or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g., shifting resources from one case onto another to ensure the latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures, and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 17 real-life data...
Time and activity sequence prediction of business process instances
Computing
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Process Model Forecasting Using Time Series Analysis of Event Sequence Data
Conceptual Modeling, 2021
Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the ambition of this paper to fill this gap. To this end, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable future state of the overall process. Such a forecast helps to investigate the consequences of drift and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding forecasting techniques can be applied. Our implementation demonstrates the accuracy of our technique on real-world event log data.
Scenario-Based Prediction of Business Processes Using System Dynamics
Many organizations employ an information system that supports the execution of their business processes. During the execution of these processes, event data are stored in the databases that support the information system. The field of process mining aims to transform such data into actionable insights, which allow business owners to improve their daily operations. For example, a process model describing the actual execution of the process can be easily extracted from the captured event data. Most process mining techniques are "backwardlooking" providing compliance and performance information. Few process mining techniques are "forward-looking". Therefore, in this paper, we propose a novel scenario-based predictive approach that allows us to assess and predict future behavior in business processes. In particular, we propose to use system dynamics to allow for "what-if" questions. We create a system dynamics model using variables trained on the basis of the past behavior of the process, as captured in the event log. This model is used to explore the effect of possibly applied changes in the process as well as roles of external factors, e.g., human behavior. Using real event data, we demonstrate the feasibility of our approach to predict possible consequences of future decisions and policies.
Process Forecasting: Towards Proactive Business Process Management
International Conference on Business Process Management, 2018
The digital economy is highly volatile and uncertain. Ever-changing customer needs and technical progress increase the pressure on organizations to continuously improve and innovate their business processes. The ability to anticipate incremental and radical process changes required in the future is a critical success factor. However, organizations often fail to forecast future business process designs and process performance. One reason is that Business Process Management (BPM) is dominated by reactive methods (e.g., lean management, traditional process monitoring), whereas there are only a few future-oriented approaches (e.g., process simulation, predictive process monitoring). This paper supports the shift towards proactive BPM by coining the notion of process forecasting-an umbrella concept for future-oriented BPM methods and techniques. We motivate the need for process forecasting by eliciting various types of process forecasting from BPM use cases and create a first understanding of its scope by providing a definition, a reference process, showing the steps to be followed in process forecasting initiatives, and a positioning against related BPM sub-areas. The definition and reference process are based on a structured literature review.
Business Processes: Behavior Prediction and Capturing Reasons for Evolution
Workflow systems are being used by business enterprises to improve the efficiency of their internal processes and enhance the services provided to their customers. Workflow models are the fundamental components of Workflow Management Systems used to define ordering, scheduling and other components of workflow tasks. Companies increasingly follow flexible workflow models in order to adapt to changes in business logic, making it more challenging to predict resource demands. In such a scenario, knowledge of what lies ahead i.e., the set of tasks that are going to be executed in the future, assists the process administration to take decisions pertaining to process management in advance. In this work, we propose a method to predict possible paths of a running instance For instances that deviate from the workflow model graph, we propose methods to determine the characteristics of the changes using classification rules.
Time prediction on multi-perspective declarative business processes
Knowledge and Information Systems, 2018
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