Unraveling the Never-Ending Story of Lifecycles and Vitalizing Processes

• URL: http://arxiv.org/abs/2407.17881v1
• Date: Thu, 25 Jul 2024 08:52:23 GMT
• Title: Unraveling the Never-Ending Story of Lifecycles and Vitalizing Processes
• Authors: Stephan A. Fahrenkrog-Petersen, Saimir Bala, Luise Pufahl, Jan Mendling,
• Abstract summary: We show the existence of lifecycle processes in many industries and that their appropriate conceptualizations pave the way for the definition of suitable modeling and analysis techniques. This paper provides a set of requirements for their analysis, and a conceptualization of lifecycle and vitalizing processes.
• Score: 2.474551220017185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Business process management (BPM) has been widely used to discover, model, analyze, and optimize organizational processes. BPM looks at these processes with analysis techniques that assume a clearly defined start and end. However, not all processes adhere to this logic, with the consequence that their behavior cannot be appropriately captured by BPM analysis techniques. This paper addresses this research problem at a conceptual level. More specifically, we introduce the notion of vitalizing business processes that target the lifecycle process of one or more entities. We show the existence of lifecycle processes in many industries and that their appropriate conceptualizations pave the way for the definition of suitable modeling and analysis techniques. This paper provides a set of requirements for their analysis, and a conceptualization of lifecycle and vitalizing processes.

Related papers

• A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches) [4.499009117849108]
  We perform a systematic review of academic literature to investigate the integration of AI/ML in business process management. In business process management and process map, AI/ML has made significant improvements using operational data on process metrics.
  arXiv  Detail & Related papers  (2024-07-07T18:26:00Z)
• Extracting Process-Aware Decision Models from Object-Centric Process Data [54.04724730771216]
  This paper proposes the first object-centric decision-mining algorithm called Integrated Object-centric Decision Discovery Algorithm (IODDA) IODDA is able to discover how a decision is structured as well as how a decision is made.
  arXiv  Detail & Related papers  (2024-01-26T13:27:35Z)
• Analyzing An After-Sales Service Process Using Object-Centric Process Mining: A Case Study [0.1433758865948252]
  This paper focuses on the emerging domain of object-centric process mining. Through an in-depth case study of Borusan Cat's after-sales service process, this study emphasizes the capability of object-centric process mining.
  arXiv  Detail & Related papers  (2023-10-16T08:34:41Z)
• Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization [0.0]
  We introduce a novel mathematically sound method that integrates theoretical process models with interrelated minimal Hidden Markov Models. Our method consolidates: (a) theoretical process models, (b) HMMs, (c) coupled nonnegative matrix-tensor factorizations, and (d) custom model selection.
  arXiv  Detail & Related papers  (2022-10-03T16:19:27Z)
• Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
  The paper presents an approach to process mining providing semi-automatic support to model optimization. A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity. We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
  arXiv  Detail & Related papers  (2022-06-10T16:20:59Z)
• OPerA: Object-Centric Performance Analysis [0.4014524824655105]
  We propose a novel approach to performance analysis by using object-centric Petri nets as formal representations of business processes. The proposed approach correctly computes existing performance metrics, while supporting the derivation of newly-introduced object-centric performance metrics. We have implemented the approach as a web application and conducted a case study based on a real-life loan application process.
  arXiv  Detail & Related papers  (2022-04-22T12:23:06Z)
• Prescriptive Process Monitoring: Quo Vadis? [64.39761523935613]
  The paper studies existing methods in this field via a Systematic Literature Review ( SLR) The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods.
  arXiv  Detail & Related papers  (2021-12-03T08:06:24Z)
• On Contrastive Representations of Stochastic Processes [53.21653429290478]
  Learning representations of processes is an emerging problem in machine learning. We show that our methods are effective for learning representations of periodic functions, 3D objects and dynamical processes.
  arXiv  Detail & Related papers  (2021-06-18T11:00:24Z)
• CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version) [62.96267257163426]
  We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework. First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
  arXiv  Detail & Related papers  (2021-03-18T20:22:50Z)
• Process Comparison Using Object-Centric Process Cubes [69.68068088508505]
  In real-life business processes, different behaviors exist that make the overall process too complex to interpret. Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes. We propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs.
  arXiv  Detail & Related papers  (2021-03-12T10:08:28Z)
• A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks [0.0]
  We develop a technique to determine the relevance scores for process activities with respect to performance measures. Annotating process models with such relevance scores facilitates a problem-focused analysis of the business process. We quantitatively evaluate the predictive quality of our technique using four datasets from different domains, to demonstrate the faithfulness of the relevance scores.
  arXiv  Detail & Related papers  (2020-08-07T12:15:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.