A5077864256- Business Process Modeling and Analysis
- Service-Oriented Architecture and Web Services
- Semantic Web and Ontologies
- Advanced Database Systems and Queries
- Data Quality and Management
- Flexible and Reconfigurable Manufacturing Systems
- Collaboration in agile enterprises
- Data Visualization and Analytics
- Big Data and Business Intelligence
- Petri Nets in System Modeling
- Software Engineering Research
- Personal Information Management and User Behavior
- Software System Performance and Reliability
- Privacy, Security, and Data Protection
- Simulation Techniques and Applications
- Data Mining Algorithms and Applications
- Internet Traffic Analysis and Secure E-voting
- Manufacturing Process and Optimization
- Data Stream Mining Techniques
- Advanced Software Engineering Methodologies
- Information Technology Governance and Strategy
- Advanced Data Processing Techniques
- Software Engineering Techniques and Practices
- Privacy-Preserving Technologies in Data
- Scheduling and Optimization Algorithms
Technical University of Denmark
2016-2024
Eindhoven University of Technology
2014-2019
Clinical Research Consortium
2019
RWTH Aachen University
2019
Universitat Politècnica de Catalunya
2019
Hasselt University
2019
The University of Melbourne
2019
Universität Innsbruck
2010-2017
University of Padua
2010-2015
Process mining techniques can be used to analyse business processes using the data logged during their execution. These are leveraged in a wide range of domains, including healthcare, where it focuses mainly on analysis diagnostic, treatment, and organisational processes. Despite huge amount generated hospitals by staff machinery involved healthcare processes, there is no evidence systematic uptake process beyond targeted case studies research context. When developing distinguishing...
It may be tempting for researchers to stick incremental extensions of their current work plan future research activities. Yet there is also merit in realizing the grand challenges one’s field. This paper presents an overview nine major problems Business Process Management discipline. These have been collected by open call community, discussed and refined a workshop setting, described here detail, including motivation why these are worth investigating. serve purpose inspiring both novice...
Accurate prediction of the completion time a business process instance would constitute valuable tool when managing processes under service level agreement constraints. Such prediction, however, is very challenging task. A wide variety factors could influence trend instance, and hence just using statistics historical cases cannot be sufficient to get accurate predictions. Here we propose new approach where, in order improve quality, both control data flow perspectives are jointly used. To...
Today's business processes are often controlled and supported by information systems. These systems record real-time about during their executions. This enables the analysis at runtime of process behavior. However, many modern produce "big data", i.e., collections data sets so large complex that it becomes impossible to store all them. Moreover, few in steady-state but, due changing circumstances, they evolve need adapt continuously. In this paper, we present a novel framework for discovery...
Process Mining represents an important research field that connects Business Modeling and Data Mining. One of the most prominent task is discovery a control-flow starting from event logs. This paper focuses on problem stream data. We propose to adapt Heuristics Miner, one effective algorithms, treatment streams Two adaptations, based Lossy Counting with Budget, as well sliding window version are proposed experimentally compared against both artificial real streams. Experimental results show...
Complex process models can hinder the comprehension of underlying business processes. While several metrics have been suggested in literature to evaluate complexity imperative models, little is known about their declarative counterparts. In this paper, we address gap through a suite that propose capture models. Following this, empirically investigate impact complexity, as measured by metrics, on users' cognitive load when comprehending Therein, use multi-modal approach including eye-tracking...
We propose a novel methodology to validate software product line (PL) models by integrating Statistical Model Checking (SMC) with Process Mining (PM). consider the feature-oriented language QFLan from PL engineering domain. allows model equipped rich cross-tree and quantitative constraints, as well aspects of dynamic PLs such staged configurations. This richness us easily obtain infinite state-space, calling for simulation-based analysis techniques, like SMC. For example, we use running...
Control flow discovery algorithms are able to reconstruct the workflow of a business process from log performed activities. These algorithms, however, do not pay attention reconstruction roles, i.e. they group activities according skills required perform them. Information about roles in processes is commonly considered important and explicitly integrated into representation, e.g. as swimlanes BPMN diagrams. This work proposes an approach enhance model with information on roles. Specifically,...
The Internet of Things (IoT) enables software-based access to vast amounts data streams from sensors measuring physical and virtual properties smart devices their surroundings. While sophisticated means for the control analysis single IoT exist, a more process-oriented view systems is often missing. Such lack process awareness hinders development process-based on top environments application mining techniques optimization in IoT. We propose framework stepwise correlation composition raw...
The increasing availability of event data recorded by information systems, electronic devices, web services and sensor networks provides detailed about the actual processes in systems organizations. Process mining techniques can use such to discover check conformance process models. For checking, we need analyze whether observed behavior matches modeled behavior. In settings, it is often desirable specify expected terms a declarative model rather than procedural model. However, models do not...
Process mining represents an important field in BPM and data research. Recently, it has gained importance also for practitioners: more companies are creating business process intelligence solutions. The evaluation of algorithms requires, as any other task, the availability large amount real-world data. Despite increasing such datasets, they affected by many limitations, primis absence a "gold standard" (i.e., reference model). This paper extends approach, already available literature,...