Relational mapping

Xem 1-20 trên 119 kết quả Relational mapping
  • Chapter 5 - Relational database design by ER and EER-to-relational mapping. This chapter includes contents: Mapping of regular entity types, mapping of weak entity types, mapping of binary 1:1 relation types, mapping of binary 1:N relationship types, mapping of binary M:N relationship types, mapping of multivalued attributes, mapping of N-ary relationship types, options for mapping specialization or generalization, mapping of union types (categories).

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  • Bài giảng Chương 5: Relational mapping trình bày về các bước triển khai sơ đồ ý niệm; sơ đồ quan hệ; ánh xạ từ lược đồ ORM thành lược đồ quan hệ; các ràng buộc trong sơ đồ quan hệ; ánh xạ ràng buộc role bắt buộc; các quy luật bảo toàn dữ liệu của mô hình quan hệ; trình tự ánh xạ từ lược đồ ORM sang lược đồ quan hệ;... Mời các bạn tham khảo.

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  • Database System: Chapter 6 - Relational Database Design by ER and EERR-to-Relational Mapping presents about ER-to-Relational Mapping Algorithm, Mapping EER Model Constructs to Relations (Options for Mapping Specialization or Generalization, Mapping of Union Types (Categories).

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  • Chapter 7 present relational database design by ER and EER-to-Relational mapping. The main contents in this chapter: ER-to-Relational mapping algorithm, mapping EER model constructs to relations. Inviting you to refer.

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  • Chapter 7 discusses how to design a relational database schema based on a conceptual schema design. This chapter presented a high-level view of the database design process, and in this chapter we focus on the logical database design or data model mapping step of database design. We present the procedures to create a relational schema from an Entity-Relationship (ER) or an Enhanced ER (EER) schema.

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  • Upon completion of this lesson, the successful participant will be able to: Overview of Relational Data Model, ER and EER-to-Relational mapping, relational algebra. Inviting you to refer lecture for more information.

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  • This chapter introduce ER- & EER-to-Relational Mapping. Upon completion of this lesson, the successful participant will be able to: Overview of Relational Data Model, ER and EER-to-Relational mapping, relational algebra. Inviting you to refer lecture for more information.

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  • JDBC has simplified database access in Java applications, but a few nagging wrinkles remain—namely, persisting Java objects to relational databases. With this book, you’ll learn how the Spring Framework makes that job incredibly easy with dependency injection, template classes, and object-relational-mapping (ORM). Through sample code, you’ll discover how Spring streamlines the use of JDBC and ORM tools such as Hibernate, the Java Persistence API (JPA), and Java Data Objects (JDO).

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  • Unique to the popular Grails web framework is its architecture. While other frameworks are built from the ground up, Grails leverages existing and proven technologies that already have advanced functionality built in. One of the key technologies in this architecture is Hibernate, on top of which Grails builds its GORM (Grails Object Relational Mapping) model layer. This provides Grails a persistence solution.

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  • Database Systems: Lecture 4 - Relational Data Model and ER-/EER-to-Relational Mapping provides about Relational Data Model, ER-/EER-to-Relational Mapping, Relational Integrity Constraints, Update Operations on Relations, Main Phases of Database Design.

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  • Chapter 4 presents Relational Data Model and ER/EER-to-Relational Mapping. Upon completion of this lesson, the successful participant will be able to: Relational Data Model, main phases of database design, ER-/EER-to-Relational Mapping. Inviting you to refer.

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  • This chapter introduce Relational algebra . After completing this chapter, students will be able to: relational operations unary , relational algebra operations from set theory, binary relational operations, additional relational operations , brief introduction to relational calculus.

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  • Microsoft’s ADO.NET Entity Framework, known widely as EF, introduced out-of-thebox Object Relational Mapping to .NET and Visual Studio. Central to Entity Framework was the Entity Data Model, a conceptual model of your application domain that maps back to the schema of your database. This conceptual model describes the core classes in your application. Entity Framework uses this conceptual model while querying from the database, creating objects from that data and then persisting changes back to the database....

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  • This Doctoral dissertation in mathematics: Coderivatives of normal cone mappings and applications this dissertation studies some proplem related to the genneralized differ - entiation theory of Mordukhovich and its applications. Our main efforts concentrate on computing or estimating the Fréchet coderivative and the Mordukhovich.

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  • For each weak entity type W in the ER schema with owner entity type E, create a relation R and include all simple attributes (or simple components of composite attributes) of W as attributes of R. In addition, include as foreign key attributes of R the primary key attribute(s) of the relation(s) that correspond to the owner entity type(s). The primary key of R is the combination of the primary key(s) of the owner(s) and the partial key of the weak entity type W, if any.

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  • Unification is often the appropriate method for expressing relations between representations in the form of feature structures; however, there are circumstances in which a different approach is desirable. A declarative formalism is presented which permits direct mappings of one feature structure into another, and illustrative examples are given of its application to areas of current interest.

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  • We present a simple linguistically-motivated method for characterizing the semantic relations that hold between two nouns. The approach leverages the vast size of the Web in order to build lexically-specific features. The main idea is to look for verbs, prepositions, and coordinating conjunctions that can help make explicit the hidden relations between the target nouns.

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  • In this paper, we explore correlation of dependency relation paths to rank candidate answers in answer extraction. Using the correlation measure, we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure. The correlations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from training.

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  • We propose a novel method for automatically interpreting compound nouns based on a predefined set of semantic relations. First we map verb tokens in sentential contexts to a fixed set of seed verbs using WordNet::Similarity and Moby’s Thesaurus. We then match the sentences with semantic relations based on the semantics of the seed verbs and grammatical roles of the head noun and modifier. Based on the semantics of the matched sentences, we then build a classifier using TiMBL.

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  • This paper describes the structure and e v a l u a t i o n of the s y n t a c t i c o - s e m a n t i c lexicon (SSL) of the German Natural Language Understanding System VIE-LANG [3]. VIE-LANG uses an SI-Net [2] as internal r e p r e s e n t a t i o n . The SSL c o n t a i n s the rules according to which the mapping between net-structures and surface s t r u c t u r...

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