This book constitutes the refereed proceedings of five workshops symposia, held at the 39th International Conference on Conceptual Modeling, ER 2020, which were supposed to be held in Vienna, Austria, in November 2020, but were held virtually due to the COVID-19 pandemic instead. The 20 papers promote and disseminate research on theories of concepts underlying conceptual modeling, methods and tools for developing and communicating conceptual models, techniques for transforming conceptual models into effective implementations, and the impact of conceptual modeling techniques on databases, business strategies and information systems. The following workshops are included in this volume: First Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making (CMAI); First International Workshop on Conceptual Modeling for Life Sciences (CMLS); Second Workshop on Conceptual Modeling, Ontologies and (Meta)data Management for Findable, Accessible, Interoperable and Reusable (FAIR) Data (CMOMM4FAIR); First Workshop on Conceptual Modeling for NoSQL Data Stores (CoMoNoS); and Third International Workshop on Empirical Methods in Conceptual Modeling (EmpER).
Author(s): Georg Grossmann; Sudha Ram
Series: Lecture Notes in Computer Science, 12584
Publisher: Springer
Year: 2021
Language: English
Pages: 650
City: Cham
Preface
ER 2020 Conference Organization
ER 2020 Workshop Organization
Empirical Methods in Conceptual Modeling (EmpER) 2020 Co-chairs
Contents
Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making (CMAI) 2020
En
How to Induce Trust in Medical AI Systems
1 Medical AI Systems
2 Formal Measures for Estimating How Well a Patient Is Covered by an AI System
3 Experiments
4 Discussion and Outlook
References
Towards Automated Support for Conceptual Model Diagnosis and Repair
1 Introduction
2 Conceptual Modeling: Learning by Feedback
3 From Model Validation to Repairs Suggestion
4 Highlighting Possibly Erroneous Decisions
5 Uncovering Error-Prone Structures
6 Conclusion and Perspectives
References
Superimposition: Augmenting Machine Learning Outputs with Conceptual Models for Explainable AI
1 Introduction
2 Background: The Problem of Explainable AI
3 Superimposition Method
4 Illustration: Superimposition Using EERD
5 Discussion and Future Work
References
Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter
1 Introduction
2 Background Work
3 Evaluation Approach
3.1 Implicit Interventional Measure
3.2 Explicit Interventional Measure
4 Experiments and Results
4.1 Experiment and Data Settings
4.2 Runtime Comparison
4.3 Rank List Similarity
4.4 Significance of Attribution Ranking
4.5 Attribution Accuracy: How Correctly Are the Right Features Attributed
5 Conclusion
References
Conceptual Modeling for Life Sciences (CMLS) 2020
En
The Importance of the Temporal Dimension in Identifying Relevant Genomic Variants: A Case Study
1 Introduction
2 Methodological Background: The SILE Method
3 Case Study: Variant Identification in Early Onset Alzheimer’s Disease
4 Conclusions and Future Work
References
Towards the Generation of a Species-Independent Conceptual Schema of the Genome
1 Introduction
2 Conceptual Schema of the Human Genome
3 Conceptual Schema of the Citrus Genome
4 Conceptual Schema of the Genome: A New Horizon
5 Conclusions
References
Conceptual Human Emotion Modeling (HEM)
1 Introduction
2 Design Considerations
3 Human Emotion Modeling: Metamodel and Language
4 Towards Embedding HEM-L into DSMLs
5 Related Work
6 Conclusions and Future Work
References
Towards an Ontology for Tertiary Bioinformatics Research Process
1 Introduction
2 State of the Art
3 Empirical Study
4 Ontology
5 Example Application
6 Conclusion
References
Using BioPAX-Parser (BiP) to Annotate Lists of Biological Entities with Pathway Data
1 Introduction
2 Background
2.1 Pathway DataBases
2.2 Pathway Enrichment Analysis Approaches
3 The BiP Algorithm and its Implementation
3.1 BiP Algorithm
3.2 BiP Implementation
4 Case Study Results
5 Conclusion and Future Work
References
Relational Text-Type for Biological Sequences
1 Introduction and Motivation
2 Conceptual Modeling and Initial Data Manipulation
3 Sequences as Relational Text-Type
4 Conclusions
References
Conceptual Modeling, Ontologies and (Meta)data Management for Findable, Accessible, Interoperable and Reusable (FAIR) Data (CMOMM4FAIR) 2020
En
Mapping the Web Ontology Language to the OpenAPI Specification
1 Introduction
2 Mapping OWL to OAS
2.1 Method for Mapping Generation
2.2 Mapping Definitions
2.3 Mapping Example
3 Mapping Implementation
4 Related Work
5 Conclusions and Future Work
References
Evaluating FAIRness of Genomic Databases
1 Introduction
2 Background
2.1 Genomic Databases
2.2 FAIR Principles and FAIRness
3 Bio FAIR Evaluator Framework
3.1 Metrics and Criteria of FAIRness Evaluation
3.2 RaCE Module
3.3 MaCE Module
4 Results and Discussion
4.1 FAIRness Experiments
4.2 Recommendations
5 Related Work
6 Conclusions
References
Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence
1 Introduction
2 The FAIR Implementation Profile Conceptual Model and Its Supporting Components
2.1 FAIR Implementation Profiles
2.2 FAIR Implementation Questionnaire
2.3 FIPs as FAIR Digital Objects
2.4 The FIP Convergence Matrix
2.5 An Emerging FIP Architecture and Workflow
3 Discussion
3.1 FIPs and FAIR Convergence
3.2 Related Work
4 Conclusion
References
Conceptual Modeling for NoSQL Data Stores (CoMoNoS) 2020
En
Deimos: A Model-Based NoSQL Data Generation Language
1 Introduction
2 Related Work
3 Rationale Behind Deimos
4 Designing the Deimos Language
5 The Generation Process
6 Conclusions and Future Work
References
Managing Physical Schemas in MongoDB Stores
1 Introduction
2 Logical Model
3 Physical Model
3.1 Physical Metamodel
3.2 Obtaining Physical Models from MongoDB
4 Mapping Between Logical and Physical Models
4.1 Obtaining Logical Models from Physical Models
4.2 Obtaining Physical Models from Logical Models
5 Related Work
6 Final Discussion
References
JSON Schema Inference Approaches
1 Introduction
2 JSON Data Format and JSON Schema
3 JSON Schema Inference Approaches
4 Comparison
5 Related Work
6 Conclusion
References
Empirical Methods in Conceptual Modeling (EmpER) 2020
En
Empirical Evaluation of a New DEMO Modelling Tool that Facilitates Model Transformations
1 Introduction
2 Background
2.1 The Demonstration Case
2.2 General DEMO Tool Requirements, Specifications and the New DMT
3 Research Method
4 Evaluation Results
5 Conclusions and Future Research Directions
References
Acquiring and Sharing the Monopoly of Legitimate Naming in Organizations, an Application in Conceptual Modeling
Abstract
1 Introduction
2 Method
3 Literature Review
3.1 Conceptual Modelling
3.2 Ontology Usefulness
3.3 Ontological Politics
3.4 Language and Power
3.5 Language and Organizational Control
3.6 Literature Gap
4 Early Findings and Discussion
4.1 Postulate 1: Modelers Should Focus on the Pragmatic Convenience of the Concepts They Define, Rather Than Debating the “Truth” of Concepts
4.2 Postulates 2: Modelers Should Spot Unspoken Areas and Concepts – Concepts for Which Words Are Lacking and Therefore, Are Not Present in the Consciousness of the Speaker
4.3 Postulate 3: In Order to Gain the Monopoly of Legitimate Naming in a Field, Modelers Must First Earn Sufficient Prestige, Reputation or Fame Within the Field
4.4 Postulates 4: Language Is not a Neutral Object and Is Intimately Tied to Power Struggles
4.5 Postulate 5: Control Over Language Can Be a Non-conflictual, Indirect Way to Exert Control Over a Group of People
5 Conclusion
References
Replicability and Reproducibility of a Schema Evolution Study in Embedded Databases
1 Introduction
2 Original Study
3 Methodology of This Study
4 Results
5 Discussion
6 Threats to Validity
7 Related Work
8 Conclusion and Future Work
References
Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas
1 Introduction
2 Examples of JSON Schema Containment
3 Methodology
3.1 Context Description
3.2 Analysis Process
4 Detailed Study Results
4.1 RQ1: What Is the Real-World Applicability of JSC-Tools?
4.2 RQ2: Which Language Features Are Difficult to Handle?
4.3 RQ3: What Is the Degree of Consensus Among JSC-Tools?
5 Discussion of Results and Research Opportunities
6 Potential Threats to Validity
7 Related Work
8 Conclusion
References
Experimental Practices for Measuring the Intuitive Comprehensibility of Modeling Constructs: An Example Design
1 Introduction
2 Comprehensibility and Intuitiveness
3 Example: Preconditions in Diagrammatic Goal Models
4 Experimental Strategy
4.1 Model Sampling
4.2 Training
4.3 Tasks
4.4 Operationalizations of Language Intuitiveness
4.5 Participant Sampling
4.6 Analysis
5 Concluding Remarks
References
Author Index