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. We also invite industry reports and vision papers. 

Topics of Interest

Specific examples of relevant topics include but are not limited to:

Foundations of conceptual modeling: 

    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 
    Logic-based knowledge representation and reasoning
    Multi-level and multi-perspective modeling 
    Ontological and cognitive foundations
    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 
    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
    Other data spaces

Conceptual modeling in:

    AI, data mining, data science, machine learning, or statistics
    Business, climate, compliance, economics, education, energy, entertainment, government, health, law, sustainability, etc
    Collaboration, crowdsourcing, games, and social networks 
    Business intelligence and analytics, Data warehousing 
    Engineering, such as agile development, requirements engineering, reverse engineering
    Enterprises, including the modeling of business rules, capabilities, goals, services, processes, and values 
    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 practices

Conceptual modeling showcased by:

    Computational tools that advance the state-of-the-art
    Empirical studies 
    Experience reports of applications, use cases, and real-world impact