Hagen Völzer
Title
Dr.
Last Name
Völzer
First name
Hagen
Email
hagen.voelzer@unisg.ch
ORCID
Phone
+41 71 224 3865
7 results
Now showing 1 - 7 of 7
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, AI-augmented Business Process Management Systems: A Research Manifesto(2023-03-31)Scopus© Citations 155 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Leveraging Digital Trace Data to Investigate and Support Human-Centered Work ProcessesThe ongoing digitization of processes in all domains of everyday life driven by IT systems shows great potential for process automation, analysis, and optimization. In the last decade process mining has advanced to an important and mature discipline of computer science research and has been widely adopted in industry. More recently,-acknowledging the huge potential of digital trace data to study processes-process science has been introduced as an interdisciplinary field studying how processes unfold over time. This paper discusses the potential that arises when using digital trace data not only in the context of highly automated processes but also to investigate humancentered (work) processes and elaborates on associated challenges. Examples range from the semi-automated storage and production processes in a smart factory to healthcare processes to process analysts performing process mining tasks and software engineers reading software artifacts like source code and process models.Type:conference paperJournal:Communications in Computer and Information ScienceScopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Leveraging Digital Trace Data to Investigate and Support Human-Centered Work Processes(Springer, 2024); ; ; ; The ongoing digitization of processes in all domains of everyday life driven by IT systems shows great potential for process automation, analysis, and optimization. In the last decade process mining has advanced to an important and mature discipline of computer science research and has been widely adopted in industry. More recently,-acknowledging the huge potential of digital trace data to study processes-process science has been introduced as an interdisciplinary field studying how processes unfold over time. This paper discusses the potential that arises when using digital trace data not only in the context of highly automated processes but also to investigate humancentered (work) processes and elaborates on associated challenges. Examples range from the semi-automated storage and production processes in a smart factory to healthcare processes to process analysts performing process mining tasks and software engineers reading software artifacts like source code and process models.Type:conference paperVolume:2028Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Supporting Provenance and Data Awareness in Exploratory Process Mining(2023-06-08); ; Scopus© Citations 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ProMiSE: Process Mining Support for End-Users(2023-07-06); ; ; ; CEUR Workshop ProceedingsIn the past decade, process mining has gained momentum in academia and the industry, as it supports organizations in deriving insights from event data recorded from process executions. The increasing adoption of process mining in practice entails supporting process analysts in their work. Indeed, their analysis includes many exploratory tasks that require them to rely on their experience to interpret the data and steer the analysis. This knowledge-intensive nature of process mining can be challenging for less experienced analysts and calls for methodological and operational guidance tailored to their needs. In this paper, we present ProMiSE, a project funded by the Swiss National Science Foundation that embraces this novel direction in process mining research. The first goal of the project is to improve our understanding of how analysts work in practice, i.e., the process of process mining. Then, methodological guidance and software-based support are developed to assist novice analysts during their analysis. The results obtained in the first two years of ProMiSE have helped to build a solid empirical basis on process mining, laying the foundation for the development of user-centered support, which we will realize in the coming years with the help of our project partners and international collaborators.Type:conference paperJournal:Proceedings of the Research Projects Exhibition Papers Presented at the 35th International Conference on Advanced Information Systems Engineering (CAiSE 2023) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Benchmark for Rule Induction in Automated Business Decisions(2024); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Fresh Approach to Analyze Process Outcomes(2023-10-23); ;