Title:
Enterprise, Business-Process and Information Systems Modeling 24th International Conference, BPMDS 2023, and 28th International Conference, EMMSAD 2023, Zaragoza, Spain, June 12-13, 2023, Proceedings
Author:
van der Aa, Han. editor.
ISBN:
9783031342417
Edition:
1st ed. 2023.
Physical Description:
XXI, 344 p. 92 illus., 64 illus. in color. online resource.
Series:
Lecture Notes in Business Information Processing, 479
Contents:
BPMDS 2023 -- Just Tell Me: Prompt Engineering in Business Process Management -- Reinforcement Learning-supported AB Testing of Business Process Improvements: An Industry Perspective -- Modelling and Execution of Data-Driven Processes with JSON-Nets -- Aligning object-centric event logs with data-centric conceptual models -- From Network Traffic Data to a business-level Event Log -- A Novel Decision Mining Method Considering Multiple Model Paths -- Modeling, Executing and Monitoring IoT-Driven Business Rules -- A Generic Approach towards Location-aware Business Process Execution -- Time-aware Contract Model for Legal Smart Contracts -- Efficient Computation of Behavioral Changes in Declarative Process Models Nicolai Schützenmeier -- Beyond Temporal Dependency: An Ontology-Based Approach to Modeling Causal Structures in Business Processes -- EMMSAD 2023 -- Principles of universal conceptual modeling -- Supporting Method Creation, Adaptation and Execution with a Low-code Approach -- IAT/ML: A Domain-Specific Approach for Discourse Analysis and Processing -- A First Validation of the Enterprise Architecture Debts Concept -- Modeling heterogeneous IT infrastructures: a collaborative component-oriented approach -- Exploring Capability Mapping as a Tool for Digital Transformation: insights from a Case Study -- TEC-MAP: a Taxonomy of Evaluation Criteria for Multi-Modeling Approaches -- Integrating Physical, Digital, and Virtual Modeling Environments in a Collaborative Design Thinking Tool -- Opportunities in Robotic Process Automation by and for Model-Driven Software Engineering -- A Requirements-Driven Framework for Automatic Data Visualization -- Comparing different visualizations for feedback on test execution in a Model-Driven Engineering environment -- Unblocking Inductive Miner - While Preserving Desirable Properties.
Added Corporate Author:
Electronic Access:
https://doi.org/10.1007/978-3-031-34241-7Copies:
Available:*
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