Predictive Monitoring of Business Processes

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital

Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments

A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make predictive process monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviors over time. As a solution to this problem, we propose the use of algorithms that allow the incremental construction of the predictive model. These incremental learning algorithms update the model whenever new cases become available so that the predictive model evolves over time to fit the current circumstances. The algorithms have been implemented using different case encoding strategies and evaluated on a number of real and synthetic datasets. The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting

Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis

A common challenge for improving business processes in large organizations is that business people in charge of the operations are lacking a fact-based understanding of the execution details, process variants, and exceptions taking place in business operations. While existing process mining methodologies can discover these details based on event logs, it is challenging to communicate the process mining findings to business people. In this paper, we present a novel methodology for discovering business areas that have a significant effect on the process execution details. Our method uses clustering to group similar cases based on process flow characteristics and then influence analysis for detecting those business areas that correlate most with the discovered clusters. Our analysis serves as a bridge between BPM people and business, people facilitating the knowledge sharing between these groups. We also present an example analysis based on publicly available real-life purchase order process data.Comment: 12 pages. Paper accepted in 23rd International Conference on Business Information Systems (BIS 2020) to be published in a proceedings edition of the Lecture Notes in Business Information Processin

Towards Designing a Conversation Mining System for Customer Service Chatbots

Chatbots are increasingly used to provide customer service. However, despite technological advances, customer service chatbots frequently reach their limits in customer interactions. This is not immediately apparent to both chatbot operators (e.g., customer service managers) and chatbot developers because analyzing conversational data is difficult and labor-intensive. To address this problem, our ongoing design science research project aims to develop a conversation mining system for the automated analysis of customer-chatbot conversations. Based on the exploration of large dataset (N= 91,678 conversations) and six interviews with industry experts, we developed the backend of the system. Specifically, we identified and operationalized important criteria for evalu-ating conversations. Our next step will be the evaluation with industry experts. Ultimately, we aim to contribute to research and practice by providing design knowledge for conversation mining systems that leverage the treasure trove of data from customer-chatbot conversations to generate valuable insights for managers and developers

Designing a Conversation Mining System for Customer Service Chatbots

As chatbots are gaining popularity in customer service, it is critically important for companies to continuously analyze and improve their chatbots’ performance. However, current analysis approaches are often limited to the question-answer level or produce highly aggregated metrics (e.g., conversations per day) instead of leveraging the full potential of the large volume of conversation data to provide actionable insights for chatbot developers and chatbot managers. To address this challenge, we developed a novel chatbot analytics approach — conversation mining — based on concepts and methods from process mining. We instantiated our approach in a conversation mining system that can be used to visually analyze customer-chatbot conversations at the process level. The results of four focus group evaluations suggest that conversation mining can help chatbot developers and chatbot managers to extract useful insights for improving customer service chatbots. Our research contributes to research and practice with novel design knowledge for conversation mining systems

Designing a Conversation Mining System for Customer Service Chatbots

As chatbots are gaining popularity in customer service, it becomes increasingly important for companies to continuously analyze and improve their chatbots’ performance. However, current analysis ap-proaches are often limited to the level of question-answer pairs or produce highly aggregated metrics (e.g., average intent scores, conversations per day) rather than leveraging the full potential of the large volume of conversation data to extract actionable insights for chatbot developers and chatbot operators (e.g., customer service managers). To address this challenge, we developed a novel chatbot analytics approach — conversation mining — based on concepts and methods from process mining. We instanti-ated our approach in a conversation mining system that can be used to visually analyze customer-chatbot conversations at the process level. The findings of four focus group evaluations show that our system can help chatbot developers and operators identify starting points for chatbot improvement. Our re-search contributes novel design knowledge for conversation mining systems

A General Framework for Predictive Business Process Monitoring

Abstract. As organizations gain awareness of the potential business value locked in their process execution event logs, "evidence-based" business process management (BPM) becomes a common tool for process analysts. In contrast to traditional process monitoring techniques which are typically performed using data from running process instances only, predictive evidence-based BPM methods tap also into historical data, to allow process workers to respond, in real-time, to specific process performance issues and compliance violations as they arise or even before they arise. In previous work, various approaches have been proposed to address typical predictive process monitoring problems, such as whether a running process instance will meet its performance targets, or when will an instance be finally finished. However, these approaches are rather ad-hoc and lack generality, as they tackle only particular, pre-defined aspects of predictive monitoring and often only work with specific characteristics of the dataset. The proposed research project aims at developing a general and robust framework for predictive process monitoring that will address a variety of process monitoring tasks such as predicting the outcome of individual activities or of the whole process instance, or predicting the completion path of an instance