By Isabelle Comyn-Wattiau, Katsumi Tanaka, Il-Yeol Song, Shuichiro Yamamoto, Motoshi Saeki
This publication constitutes the refereed complaints of the 345h overseas convention on Conceptual Modeling, ER 2016, held in Gifu, Japan, in November 2016.
The 23 complete and 18 brief papers provided including three keynotes have been conscientiously reviewed and chosen from 113 submissions. The papers are prepared in topical sections on Analytics and Conceptual Modeling; Conceptual Modeling and Ontologies; requisites Engineering; complex Conceptual Modeling; Semantic Annotations; Modeling and Executing enterprise procedures; enterprise strategy administration and Modeling; functions and Experiments of Conceptual Modeling; Schema Mapping; Conceptual Modeling assistance; and objective Modeling.
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Extra resources for Conceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November 14-17, 2016, Proceedings
92–104. IEEE (2013). ISBN 978-1-4673-5765-4/13 22. L:. Deciphering the genome: community driven approaches. In: BioIT World, Boston, MA (2016). com/ 23. : ClinGen: the clinical genome resource. New Engl. J. Med. Spec. Rep. NEJM 372, 2235–2242 (2015) 24. : Conceptual modeling of human genome: integration challenges. -D. ) Conceptual Modelling and Its Theoretical Foundations. LNCS, vol. 7260, pp. 231–250. ca Abstract. Data analytics is an essential element for success in modern enterprises. Nonetheless, to eﬀectively design and implement analytics systems is a non-trivial task.
The notion of foundational ontology is essential. A foundational ontology can be deﬁned as an ontology that “deﬁnes a range of top-level domain-independent ontological categories, which form a general foundation for more elaborated domain-speciﬁc ontologies” [12, 13]. Rephrasing Guizzardi in , on the basis of a foundational ontology “a domain ontology is constructed with the goal of making the best possible description of the domain in reality”. It can be represented as a conceptual model, as an engineering artifact with the additional requirement of representing a model of consensus within a community.
A relational model of data for large shared data banks. ACM Commun. 13, 377–387 (1970) 4. : ORA-SS: an object-relationshipattribute model for semistructured data. Technical report, National University of Singapore (2000) 5. , Ling, T. : Extending and inferring functional dependencies in schema transformation. In: CIKM (2004) 6. , Discover: keyword search in relational databases. In: VLDB (2002) 7. : Keyword searching and browsing in databases using BANKS. In: ICDE (2002) 8. : Schema-independence in XML keyword search.
Conceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November 14-17, 2016, Proceedings by Isabelle Comyn-Wattiau, Katsumi Tanaka, Il-Yeol Song, Shuichiro Yamamoto, Motoshi Saeki