Related papers: Advancements and Challenges in Object-Centric Proc…
Event logs are invaluable for conducting process mining projects, offering insights into process improvement and data-driven decision-making. However, data quality issues affect the correctness and trustworthiness of these insights, making…
The aim of this paper is to carry out a systematic analysis of the literature to show the state of the art of Business Processes Management Systems (BPMS). BPMS represents a technology that automates business processes connecting users with…
Dynamic model inference techniques have been the center of many research projects recently. There are now multiple open source implementations of state-of-the-art algorithms, which provide basic abstraction and merging capabilities. Most of…
Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep learning architectures. In this paper, we extend our prior LLM-based Predictive…
Process analytic approaches play a critical role in supporting the practice of business process management and continuous process improvement by leveraging process-related data to identify performance bottlenecks, extracting insights about…
Process mining is a field of computer science that deals with discovery and analysis of process models based on automatically generated event logs. Currently, many companies use this technology for optimization and improving their…
Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability of data and recent advances in machine learning and…
Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive…
The uptake of Artificial Intelligence (AI) impacts the way we work, interact, do business, and conduct research. However, organizations struggle to apply AI successfully in industrial settings where the focus is on end-to-end operational…
Systematic testing of object-oriented software turned out to be much more complex than testing conventional software. Especially the highly incremental and iterative development cycle demands both many more changes and partially implemented…
Context: Data Mining (DM) method has been evolving year by year and as of today there is also the enhancement of DM technique that can be run several times faster than the traditional one, called Distributed Data Mining (DDM). It is not a…
Modern information systems are able to collect event data in the form of event logs. Process mining techniques allow to discover a model from event data, to check the conformance of an event log against a reference model, and to perform…
Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration…
Process mining techniques such as process discovery and conformance checking provide insights into actual processes by analyzing event data that are widely available in information systems. These data are very valuable, but often contain…
Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to its expected or desirable outcomes. Deviant executions of a business process…
Process Mining is a branch of Data Science that aims to extract process-related information from event data contained in information systems, that is steadily increasing in amount. Many algorithms, and a general-purpose open source…
The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model. Nonetheless,…
Insufficient requirements reusability, understandability and verifiability jeopardize software projects. Empirical studies show little success in improving these qualities separately. Applying object-oriented thinking to requirements leads…
Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement…
The discipline of process mining deals with analyzing execution data of operational processes, extracting models from event data, checking the conformance between event data and normative models, and enhancing all aspects of processes.…