Call for Papers
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.
- Full paper abstracts submission (mandatory): 17 May, 2023
- Full paper submission: 24 May, 2023
- Author notification: 24 July, 2023
- Camera-ready papers and author registration: 11 September, 2023
All deadlines are 23:59 AoE (Anywhere on Earth)
Submission Guidelines for Full paper and Review Process
Papers proposals must be submitted via the official ER submission page on Easychair to the track “ER 2023 Full Papers”.
Since the proceedings will be published by Springer in the LNCS series, authors must submit manuscripts using the LNCS style (see style files and details). Springer has provided a LaTeX template in Overleaf for your convenience. The page limit for submitted papers (as well as for final, camera-ready papers) is 16 pages (excluding references).
Manuscripts not submitted in the LNCS style or exceeding the page limit will be desk rejected. Likewise, submissions that do not primarily focus on aspects of conceptual modeling shall be rejected without formal reviews. Papers submitted must not be under evaluation for or have already been published in, or accepted for publication, in a journal or another conference.
Each paper admitted to the review process will be reviewed by at least three committee members in a double-blind process, with a meta-review provided by a program board member.
Research papers will be assessed for the extent of contribution, grounding in the literature, novelty, presentation quality, relevance, and technical rigor. Industry reports should demonstrate the impact of conceptual modeling in a real-world setting, arguing for generalisability of methods and lessons learned. Vision papers should describe an ambitious and credible future state of conceptual modeling, articulating the need, research plan, and potential impact of the vision.
Summary of Changes
If you are a returning author to ER, please note the following changes with respect to the previous year:
- The review process is double-blind and hence submissions must be anonymized; and,
- Papers are limited to 16 pages excluding references.
Post-Conference Special Journal Issue
The authors of selected papers will be invited to prepare a substantially revised and extended version to a Special Issue in Elsevier’s Data & Knowledge Engineering (JCR 2020 Impact Factor 1.992).
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