Related papers: Automated simulation and verification of process m…
The shift toward IoT-enabled, sensor-driven systems has transformed how operational data is generated, favoring continuous, real-time event streams (ES) over static event logs. This evolution presents new challenges for Streaming Process…
The analysis of fairness in process mining is a significant aspect of data-driven decision-making, yet the advancement in this field is constrained due to the scarcity of event data that incorporates fairness considerations. To bridge this…
Business processes need to have certain constraints such that they can lead to sustainable outcomes. These constraints can be manifold and their adherence has to be monitored. In the past compliance checking has been applied in several…
The paper presents an approach to verification of a multi-agent data analysis algorithm. We base correct simulation of the multi-agent system by a finite integer model. For verification we use model checking tool SPIN. Protocols of agents…
Monitoring of industrial processes is a critical capability in industry and in government to ensure reliability of production cycles, quick emergency response, and national security. Process monitoring allows users to gauge the progress of…
Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the…
Data-driven analysis of business processes has a long tradition in research. However, recently the term of process mining is mostly used when referring to data-driven process analysis. As a consequence, awareness for the many facets of…
Process mining supports the analysis of the actual behavior and performance of business processes using event logs. % such as, e.g., sales transactions recorded by an ERP system. An essential requirement is that every event in the log must…
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.…
Process mining has grown popular today given their ability to provide managers with insights into the actual business process as executed by employees. Process mining depends on event logs found in process aware information systems to model…
Process mining provides techniques to improve the performance and compliance of operational processes. Although sometimes the term "workflow mining" is used, the application in the context of Workflow Management (WFM) and Business Process…
In a Web Advertising Traffic Operation it's necessary to manage the day-to-day trafficking, pacing and optimization of digital and paid social campaigns. The data analyst on Traffic Operation can not only quickly provide answers but also…
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 discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multiple permutations, increasing data requirements, especially for…
In modern IT systems and computer networks, real-time and offline event log analysis is a crucial part of cyber security monitoring. In particular, event log analysis techniques are essential for the timely detection of cyber attacks and…
Transaction-level modeling with SystemC has been very successful in describing the behavior of embedded systems by providing high-level executable models, in which many of them have inherent probabilistic behaviors, e.g., random data and…
This paper advocates for guiding an effective system implementation approach at a business process level. It details a case study of a food product manufacturer that transitioned to a new local information system. 41 units' data (10160…
This article introduces a fully automated verification technique that permits to analyze real-time systems described using a continuous notion of time and a mixture of operational (i.e., automata-based) and descriptive (i.e., logic-based)…
Predictive maintenance is used in industrial applications to increase machine availability and optimize cost related to unplanned maintenance. In most cases, predictive maintenance applications use output from sensors, recording physical…
We suggest systems mining as the next step after process mining. Systems mining starts with a more careful investigation of runs, and constructs a detailed model of behavior, more subtle than classical process mining. The resulting model is…