The Experts below are selected from a list of 220731 Experts worldwide ranked by ideXlab platform
Gottfried Vossen - One of the best experts on this subject based on the ideXlab platform.
-
a survey of Database Design transformations based on the entity relationship model
Data and Knowledge Engineering, 1995Co-Authors: Christian Fahrner, Gottfried VossenAbstract:Abstract At present, the Entity-Relationship (ER) model is the most important paradigm for conceptual Database Design. Since the model was introduced in the mid-seventies, a large body of literature has been published on transforming conceptual ER schemas or diagrams into logical data models. The purpose of this paper is to survey this literature. A first focus is on transformation approaches from the ER model to traditional data models, i.e. to the relational, the network, or the hierarchical model; this is then complemented by a discussion of more recent transformations to object-oriented models. A second focus is on considering the process of reverse engineering, i.e. transformations from a logical model back into the ER model; finally, an overview of direct transformations between various logical data models is presented.
Veda C. Storey - One of the best experts on this subject based on the ideXlab platform.
-
the role of domain ontologies in Database Design an ontology management and conceptual modeling environment
ACM Transactions on Database Systems, 2006Co-Authors: Vijayan Sugumaran, Veda C. StoreyAbstract:Database Design is difficult because it involves a Database Designer understanding an application and translating the Design requirements into a conceptual model. However, the Designer may have little or no knowledge about the application or task for which the Database is being Designed. This research presents a methodology for supporting Database Design creation and evaluation that makes use of domain-specific knowledge about an application stored in the form of domain ontologies. The methodology is implemented in a prototype system, the Ontology Management and Database Design Environment. Initial testing of the prototype illustrates that the incorporation and use of ontologies is effective in creating entity-relationship models.
-
Database Design with common sense business reasoning and learning
ACM Transactions on Database Systems, 1997Co-Authors: Veda C. Storey, Roger H L Chiang, Debabrata Dey, Robert C Goldstein, Shankar SudaresanAbstract:Automated Database Design systems embody knowledge about the Database Design process. However, their lack of knowledge about the domains for which Databases are being developed significantly limits their usefulness. A methodology for acquiring and using general world knowledge about business for Database Design has been developed and implemented in a system called the Common Sense Business Reasoner, which acquires facts about application domains and organizes them into a a hierarchical, context-dependent knowledge base. This knowledge is used to make intelligent suggestions to a user about the entities, attributes, and relationships to include in a Database Design. A distance function approach is employed for integrating specific facts, obtained from individual Design sessions, into the knowledge base (learning) and for applying the knowledge to subsequent Design problems (reasoning).
-
relational Database Design based on the entity relationship model
Data and Knowledge Engineering, 1991Co-Authors: Veda C. StoreyAbstract:Abstract The Entity-Relationship (E-R) model is often recommended for use in the Database Design process because its concepts are perceived to be both natural and easy to use. This paper describes a methodology for the Design of a relational Database based on the E-R model. The methodology starts with the identification of the basic E-R constructs that occur in an application resulting in an initial Database Design. Next, ways of capturing certain semantics of an application through data abstraction and procedures for identifying potential Design problems are discussed. The translation of the E-R model into a set of normalized relations completes the Design process. This methodology builds on previous work on the Extended Entity-Relationship Model and on the automation of Database Design.
Michael L Gibson - One of the best experts on this subject based on the ideXlab platform.
-
a laboratory experiment on integrating conceptual logical data modeling with object oriented Database Design principles
Journal of Computer Information Systems, 2016Co-Authors: Thomas E Marshall, Michael L GibsonAbstract:(1996). A Laboratory Experiment on Integrating Conceptual/Logical Data Modeling with Object-Oriented Database Design Principles. Journal of Computer Information Systems: Vol. 36, No. 3, pp. 83-94.
-
technology versus methodology support for Database Design a study of Designer choice related to perception and performance
Journal of Database Management, 1996Co-Authors: Thomas E Marshall, Michael L GibsonAbstract:Information Systems (IS) Designers often have to choose between directly applying Design methods and/or using technology to support Design methods. The purpose of this research is to develop a better understanding of how the choice between Design approaches - Design methods versus advanced technology - may impact the perceptions and performance of IS Designers. The literature suggests that the scope of criteria useful in evaluating Design methods and approaches is expanding. Research into methodology and technology support should include aspects addressing how a Design approach impacts the Designer, facilitates Designeruser communications, and supports the implementation process of transforming conceptual models into more computeroriented forms. Through a laboratory experiment, this research investigates Database Design support by comparing a data modeling Design approach (Kroenke Semantic Object approach) to a technology-based approach (Analyst Designer software). The research findings provide insights into perceived and performance- based support provided by the different Design approaches. In general, performance and perceptions with respect to type of support are not entirely consistent. There appears to be an underlying theme addressing the trade-offs between usability and the technical implementation support provided. When possible dysfunctional consequences exist regarding these trade-offs, IS managers should communicate to their Designers that implementation integrity should not be sacrificed for increased usability, or vice-versa. Database Designers need to be aware of these issues in Design methods, especially those related to Design performance, and seek means of mitigating any negative impact on the integrity of the Databases Designed.
Christian Fahrner - One of the best experts on this subject based on the ideXlab platform.
-
a survey of Database Design transformations based on the entity relationship model
Data and Knowledge Engineering, 1995Co-Authors: Christian Fahrner, Gottfried VossenAbstract:Abstract At present, the Entity-Relationship (ER) model is the most important paradigm for conceptual Database Design. Since the model was introduced in the mid-seventies, a large body of literature has been published on transforming conceptual ER schemas or diagrams into logical data models. The purpose of this paper is to survey this literature. A first focus is on transformation approaches from the ER model to traditional data models, i.e. to the relational, the network, or the hierarchical model; this is then complemented by a discussion of more recent transformations to object-oriented models. A second focus is on considering the process of reverse engineering, i.e. transformations from a logical model back into the ER model; finally, an overview of direct transformations between various logical data models is presented.
Shamkant B Navathe - One of the best experts on this subject based on the ideXlab platform.
-
graph Database Design challenges using hpc platforms
IEEE International Conference on High Performance Computing Data and Analytics, 2012Co-Authors: Prajakta Kalmegh, Shamkant B NavatheAbstract:Graph Database Management Systems, also called graph Databases, have recently gained popularity in the Database research community due to a need to effectively manage large scale data with inherent graph-like properties. Graph Databases are representation, storage and querying systems for naturally occurring graph structures. Graph Databases are finding increasing applications in social networks, computational geometry, bioinformatics, drug discovery, semantic web applications and so on. The intent of this paper is to introduce the role of graph Database management systems in the context of high-performance computing platforms and present the options for Designing high-performance graph Databases. We also present a set of potential research directions and list the challenges in combining the research in the two fields of graph Databases and high-performance computing.
-
conceptual Database Design an entity relationship approach
1991Co-Authors: Carlo Batini, Stefano Ceri, Shamkant B NavatheAbstract: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
-
conceptual Database Design an entity relationship approach
1991Co-Authors: Carlo Batini, Stefano Ceri, Shamkant B NavatheAbstract: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
