Modeling and Reasoning over Declarative Data-Aware Processes with Object-Centric Behavioral Constraints

Artale, Alessandro; Kovtunova, Alisa; Montali, Marco; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-26619-6_11

Cited by 27 publications

(16 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, declarative and procedural process modeling formalisms have been extended with data inspection and manipulation capabilities. This led to a flourishing series of approaches, ranging from case variables and decisions [4,14,29] to process networks that co-evolve multiple objects involved in one-tomany and many-to-many relationships [2,25,26,34,64,74], which called for novel process mining techniques [53,92]. Recently, also the behavioral influence of actors and resources on the processes they perform has been investigated from the angle of performance.…”

Section: Perspective Agilitymentioning

confidence: 99%

AI-Augmented Business Process Management Systems: A Research Manifesto

Dumas¹,

Fournier²,

Limonad³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems that draws upon trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and contextsensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.CCS Concepts: • Applied computing → Business process management; • Information systems → Middleware business process managers; Information systems applications; • Software and its engineering → Software development process management; • Computing methodologies → Artificial intelligence; Modeling and simulation.

show abstract

Section: Perspective Agilitymentioning

confidence: 99%

AI-Augmented Business Process Management Systems: A Research Manifesto

Dumas¹,

Fournier²,

Limonad³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a proof-of-concept, we considered the standard relational database technology and implemented a Java-based tool, using the Camunda BPMN model API 7 . The tool parses and labels a BPMN 2.0 XML process description and is interfaced with a relational database implemented using Post-greSQL 8 . The database was implemented by following the schema in Figure 2, but translating all the fields containing complex set-valued attributes into many-to-many relations.…”

Section: B Proof-of-concept: Implementation With Postgresqlmentioning

confidence: 99%

“…However, the integrated modeling, verification, enactment, and analysis of these two perspectives still presents relevant research challenges [4], [7], [9], [20]. Among process modeling approaches, we can distinguish between those aiming to enhance activity-centric processes for the modeling and execution of the data perspective [3], [9], [10], [13], [40] and those focusing on defining languages for data-or object-aware process modeling, verification and enactment [4], [7], [8], [14], [41], [42]. Our approach falls within the first research line and, particularly, aims to define a conceptual and unified view of a process model and database schema that can be used to explore the data perspective of a process at design-time.…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, linking business processes and data is still an open challenge [7], [8], especially when considering conceptual models [2], [7], [9]- [11]. Connecting process models and data at the conceptual level brings many advantages: it improves the understanding of the overall process in a general, tool-agnostic way [7] and the modeling of data sources supporting process C. Combi, B. Oliboni are with the Department of Computer Science, University of Verona, Italy, e-mail: {carlo.combi | barbara.oliboni@univr.it}; F. Zerbato is with the Institute of Computer Science, University of St. Gallen, Switzerland, e-mail: francesca.zerbato@unisg.ch.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Exploration of Data-Intensive Business Processes

Combi¹,

Oliboni²,

Zerbato³

2021

IEEE Trans. Serv. Comput.

View full text Add to dashboard Cite

Modeling and reasoning over business processes require enterprises to manage and integrate large amounts of information. Despite process designers and engineers may benefit from a unified view of process and data models, integrating these two perspectives is challenging, especially when considering conceptual models. In this paper, we provide a uniform formal representation of a process model, the schema of a related database, and the data operations connecting them. Then, we show how we can use such a formal representation to identify interesting information during the integrated conceptual modeling and analysis of processes and related databases, from a process (re-)design and improvement perspective. Finally, we discuss the evaluation of the proposed approach through a controlled experiment and a proof-of-concept implementation that considers both relational and XML database technologies.

show abstract

“…Processes can be graphically represented by process models [5] which explain responsibilities, inspect compliance, predict performance using simulation [6], manage complexity, reduce variation, and enhance coordination [5] in processes. Discovering process models or process discovery from event logs is one of the main tasks in process mining.…”

Section: Introductionmentioning

confidence: 99%

Discovering care pathways for multi-morbid patients using event graphs

Naeimaei¹,

Mannhardt²,

Toussaint³

2021

Preprint

View full text Add to dashboard Cite

Patients suffering from multiple diseases (multi-morbid patients) often have complex clinical pathways. They are diagnosed and treated by different specialties and undergo other clinical actions related to various diagnoses. Coordination of care for these patients is often challenging, and it would be of great benefit to get better insight into how the clinical pathways develop in reality. Discovering these pathways using traditional process mining techniques and standard event logs may be difficult because the patient is involved in several highly independent clinical processes. Our objective is to explore the potential of analyzing these pathways using an event log representation reflecting the independent clinical processes. Our main research question is: How can we identify valuable insights by using a multi-entity event data representation for clinical pathways of multi-morbid patients? Our method was built on the idea to represent multiple entities in event logs as event graphs. The MIMIC-III dataset was used to evaluate the feasibility of this approach. Several clinical entities were identified and then mapped into an event graph. Finally, multi-entity directly follows graphs were discovered by querying the event graph visualizing them. Our result shows that paths involving multiple entities include traditional process mining concepts not for one clinical process but all involved processes. In addition, the relationship between activities of different clinical processes, which was not recognizable in traditional models, is visible in the event graph representation.

show abstract

Modeling and Reasoning over Declarative Data-Aware Processes with Object-Centric Behavioral Constraints

Cited by 27 publications

References 21 publications

AI-Augmented Business Process Management Systems: A Research Manifesto

AI-Augmented Business Process Management Systems: A Research Manifesto

Integrated Exploration of Data-Intensive Business Processes

Discovering care pathways for multi-morbid patients using event graphs

Contact Info

Product

Resources

About