International Conference on Conceptual Modelling

Overview

We welcome submissions of original research on a variety of topics on conceptual modeling. These include well-established areas of research and practice, such as modeling languages and techniques, model theories, methods and tools for developing, transforming, implementing and communicating conceptual models. Submissions that lead to new foundations, links, applications, or enlarge current boundaries of conceptual modeling are especially welcome. In celebrating the conference’s 45th anniversary this year, we especially invite contributions on the theme of CONCEPTUAL MODELING AND SUSTAINABILITY. Conceptual modeling plays a critical role in sustainability by offering structured ways to represent and connect complex environmental, social, and economic systems. In a year marked by accelerating climate impacts, policy transitions, and technological change, models help researchers and practitioners align assumptions, compare scenarios, and translate knowledge across disciplines. Topics of interest include foundations of conceptual models, theories of concepts, ontology-driven conceptual modeling and analysis, methods and tools for developing, communicating, consolidating, and evolving conceptual models, and techniques for transforming conceptual models into effective implementations. Specific examples of many other relevant topics include (but are not limited to): Foundations of conceptual modeling: Human-centred and inclusive modeling; Model explainability and transparency; Automated and AI-assisted conceptual modeling; Complexity management of large conceptual models; Concept formalization, including data manipulation languages and techniques, formal concept analysis, and integrity constraints; Domain-specific modeling; Discovery of models, (anti-)patterns, and structures; Evolution, exchange, integration, and transformation of models; Justification and evaluation of models; Interactive, dynamic, and adaptive modeling systems; Logic-based knowledge representation and reasoning; Multi-level and multi-perspective modeling; Ontological and cognitive foundations; Knowledge graphs and reasoning; Quality paradigms and metrics; Semantics in conceptual modeling; Theories and methodologies for conceptual modeling; Verification and validation of conceptual models. Conceptual modeling for: Data access, acquisition, integration, maintenance, preparation, transformation, and visualization; Data management, including database design, performance optimization, privacy and security, provenance, transactions, queries; Data value, variety, velocity, veracity, volume, and other dimensions; Data-centric AI development; Distributed, decentralized, ledger-based, parallel, and P2P databases; Graph and network databases; Object-oriented and object-relational databases; SQL, NewSQL, and NoSQL databases; Spatial and temporal databases; Event-based and stream architectures; Multimedia and text databases; Approximate, probabilistic, and uncertain databases; Web, Semantic Web, knowledge graphs, and cloud databases; Synthetic data and simulation modeling; Other data spaces. Conceptual modeling in: AI, data mining, data science, machine learning, explainable AI, LLMs, statistics; Business, climate, compliance, economics, education, energy, entertainment, government, health care, law, sustainability, supply chains, etc.; Collaboration, crowdsourcing, games, and social networks; Business intelligence and analytics, Data warehousing; Engineering, such as agile development, requirements engineering, reverse engineering, and model-driven engineering; Enterprises, including the modeling of business rules, capabilities, goals, services, processes, values, software, and systems; Ethics, fairness, responsibility, or trust; Digital twins, fog and edge computing, Industry 4.0, Internet of Things; Information classification, filtering, retrieval, summarization, and visualization; Scientific data management, including FAIR scientific data practices.

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