Conceptual Modeling Er 2010
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Conceptual Modeling – ER 2010
Author: Jeffrey Parsons
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-10-19
This book constitutes the refereed proceedings of the 29th International Conference on Conceptual Modeling, ER 2010, held in Vancouver, BC, Canada, in November 2010. The 32 revised full papers presented were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on business process modeling; requirements engineering and modeling 1; requirements engineering and modeling 2; data evolution and adaptation; operations on spatio-temporal data; demos and posters; model abstraction, feature modeling, and filtering; integration and composition; consistency, satisfiability and compliance checking; using ontologies for query answering; and document and query processing.
Conceptual Modeling – ER 2010
th This publication comprises the proceedings of the 29 International Conference on Conceptual Modeling (ER 2010), which was held this year in Vancouver, British Columbia, Canada. Conceptual modeling can be considered as lying at the confluence of the three main aspects of information technology applications –– the world of the stakeholders and users, the world of the developers, and the technologies available to them. C- ceptual models provide abstractions of various aspects related to the development of systems, such as the application domain, user needs, database design, and software specifications. These models are used to analyze and define user needs and system requirements, to support communications between stakeholders and developers, to provide the basis for systems design, and to document the requirements for and the design rationale of developed systems. Because of their role at the junction of usage, development, and technology, c- ceptual models can be very important to the successful development and deployment of IT applications. Therefore, the research and development of methods, techniques, tools and languages that can be used in the process of creating, maintaining, and using conceptual models is of great practical and theoretical importance. Such work is c- ducted in academia, research institutions, and industry. Conceptual modeling is now applied in virtually all areas of IT applications, and spans varied domains such as organizational information systems, systems that include specialized data for spatial, temporal, and multimedia applications, and biomedical applications.
Conceptual Modeling of Information Systems
Author: Antoni Olivé
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-08-15
It is now more than fifty years since the first paper on formal specifications of an information system was published by Young and Kent. Even if the term “conceptual model” was not used at that time, the basic intention of the abstract specification was to a large extent the same as for developing conceptual models today: to arrive at a precise, abstract, and hardware - dependent model of the informational and time characteristics of a data processing problem. The abstract notation should enable the analyst to - ganize the problem around any piece of hardware. In other words, the p- pose of an abstract specification was for it to be used as an invariant basis for designing different alternative implementations, perhaps even using different hardware components. Research and practice of abstract modeling of information systems has since the late fifties progressed through many milestones and achie- ments. In the sixties, pioneering work was carried out by the CODASYL Development committee who in 1962 presented the “Information Al- bra”. At about the same time Börje Langefors published his elementary message and e-file approach to specification of information systems. The next decade, the seventies, was characterized by the introduction of a large number of new types of, as they were called, “data models”. We saw the birth of, for instance, Binary Data Models, Entity Relationship Models, Relational Data Models, Semantic Data Models, and Temporal Deductive Models.