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 equipped in topical sections on Analytics and Conceptual Modeling; Conceptual Modeling and Ontologies; specifications Engineering; complex Conceptual Modeling; Semantic Annotations; Modeling and Executing enterprise approaches; enterprise method administration and Modeling; functions and Experiments of Conceptual Modeling; Schema Mapping; Conceptual Modeling counsel; and objective Modeling.

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Extra info for Conceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November 14-17, 2016, Proceedings

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1007/BFb0020552 13. : Discovering semantics from data-centric XML. 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.

5. The data graph for the RDB in Fig. 4 Incomplete Object Answer. Suppose a user issues the keyword query Q1 = {Steven} to retrieve all the information about him. , the tuple L3 in the Lecturer relation. However, information of his multivalued attributes such as degrees, majors, universities and years of Steven, which are stored in the Qualification relation, are not retrieved. Incomplete Relationship Answer. Suppose a user wants to know the information of the course where a student Bill obtains grade A, and issues the keyword query Q2 = {Bill A}.

During analytics projects, one needs to make design choices such as what are potential algorithms that can address the problem at hand? What criteria should be considered to evaluate those algorithms? What/how data should be prepared to be used by algorithms? These decisions have important implications in several aspects of the eventual analytics solution, such as scalability, understandability, tolerance to noisy data and missing values. On the other hand, aligning analytics with business strategies is critical for achieving value through analytics [14,17].

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