Topic
Semi-structured model
About: Semi-structured model is a research topic. Over the lifetime, 2221 publications have been published within this topic receiving 64867 citations.
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Book•
17 Oct 2013TL;DR: A data model, called the entity-relationship model, is proposed that incorporates some of the important semantic information about the real world and can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model.
Abstract: A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information in the real world. A special diagramatic technique is introduced as a tool for data base design. An example of data base design and description using the model and the diagramatic technique is given. Some implications on data integrity, information retrieval, and data manipulation are discussed.The entity-relationship model can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented.
5,941 citations
22 Sep 1975
TL;DR: A data model, called the entity-relationship model, which incorporates the semantic information in the real world is proposed, and a special diagramatic technique is introduced for exhibiting entities and relationships.
Abstract: A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information about the real world. A special diagrammatic technique is introduced as a tool for database design. An example of database design and description using the model and the diagrammatic technique is given. Some implications for data integrity, information retrieval, and data manipulation are discussed.The entity-relationship model can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented.
3,693 citations
03 Jun 2002
TL;DR: This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system, and proposes three order encoding methods that can be used to represent XML order in the relational data model, and also proposes algorithms for translating ordered XPath expressions into SQL using these encoding methods.
Abstract: XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using relational database systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.
2,402 citations
Proceedings Article•
11 Sep 2001TL;DR: This paper proposes a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches.
Abstract: Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems.
1,533 citations
Book•
17 Aug 1991TL;DR: This chapter discusses Conceptual Design, Logical Design, and Design Tools for Database Design, as well as Joint Data and Functional Analysis, and Improving the Quality of a Database Schema.
Abstract: I. CONCEPTUAL DATABASE DESIGN. 1. An Introduction to Database Design. 2. Data Modeling Concepts. 3. Methodologies for Conceptual Design. 4. View Design. 5. View Integration. 6. Improving the Quality of a Database Schema. 7. Schema Documentation and Maintenance. II. FUNCTIONAL ANALYSIS FOR DATABASE DESIGN. 1. Functional Analysis Using the Dataflow Model. 2. Joint Data and Functional Analysis. 3. Case Study. III. LOGICAL DESIGN AND DESIGN TOOLS. 1. High-Level Logical Design Using the Entity-Relationship Model. 2. Logical Design for the Relational Model. 3. Logical Design for the Network Model. 4. Logical Design for the Hierarchical Model. 5. Database Design Tools. Index. 0805302441T04062001
1,018 citations