By Isabelle Comyn-Wattiau, Katsumi Tanaka, Il-Yeol Song, Shuichiro Yamamoto, Motoshi Saeki
This e-book constitutes the refereed complaints of the 345h foreign 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 rigorously reviewed and chosen from 113 submissions. The papers are prepared in topical sections on Analytics and Conceptual Modeling; Conceptual Modeling and Ontologies; necessities Engineering; complicated Conceptual Modeling; Semantic Annotations; Modeling and Executing company strategies; company approach administration and Modeling; functions and Experiments of Conceptual Modeling; Schema Mapping; Conceptual Modeling counsel; and aim Modeling.
Read or Download Conceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November 14-17, 2016, Proceedings PDF
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Additional resources for Conceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November 14-17, 2016, Proceedings
M. ) DEXA 2013. LNCS, vol. 8055, pp. 88–102. Springer, Heidelberg (2013). doi:10. 1007/978-3-642-40285-2 10 14. : An analysis of multivalued and join dependencies based on the entityrelationship approach. Data Knowl. Eng. 1, 253–271 (1985) 15. : A normal form object-oriented entity relationship diagram. In: Loucopoulos, P. ) ER 1994. LNCS, vol. 881, pp. 241–258. Springer, Heidelberg (1994). 1007/3-540-58786-1 83 16. : An improved third normal form for relational databases. ACM Trans. Database Syst.
The former indicates the common lecturer while the latter indicates the common course of these two students. This can be confusing as two similar queries have inconsistent answers. The reason is that existing keyword search methods do not interpret the user’s search intention and blindly return Steiner trees of keyword match nodes. Schema Dependence. Given the same data source, the relations in RDB are often denormalized to improve runtime performance. This denormalization leads to data duplication and aﬀects the database schema.
Towards this end, discovering the business goals and translating them into analytics goals is a critical step [4,15]. , a set of metamodels and a set of design catalogues) for overcoming these challenges. The framework includes three complementary modeling views: (i) The Business View represents an enterprise in terms of strategies, decisions, analytics questions, and required insights. This view is used to systematically elicit analytics requirements and to inform the types of analytics that the user needs.