A contribution to IS general taxonomy (original) (raw)
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The challenge of defining information system
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This article explores the field of artificial intelligence and intellectual systems. The structure, types and classification of the intellectual system are studied. The intellectual system is a technical or software system that is capable of solving the problems which are specific and creative in a particular subject area, so that knowledge is stored in the memory of such systems. Intelligent System is a technical or software system that competently solves the problems that are relevant to a particular subject area and that knowledge is stored in such systems. Intelligent systems are studied by a team of researchers called "artificial intelligence". The article provides the differences between the ordinary system and the intellectual system. The system is intellectual when it not only changes the parameters of information access, but also changes its behavior itself depending on the system capability. Intelligent system in decision-making technologies is an intellectual information-computing system that solves the problems without human involvement. The classification of intellectual systems is analyzed in the article. A conceptual model for intelligent systems has been developed. The intellectual systems themselves may also have some gaps. In future, the development of new intelligence systems and improvement of existing ones will benefit the economy even more. Contribution/Originality: The intelligent system structure, types of program errors, classification of programming errors, research areas of intellectual systems was studied. Based on the study, a conceptual model for intellectual systems has been developed.
1986
Most large systems development efforts proceed in a top-down fashion where initial specifications and requirements are incorporated into a high-level design, followed by programs based on this design. However, a major part of the software life-cycle effort is devoted to maintenance. While several existing methodologies aid in the initial phases of requirements and specification, they have proven to be of little value for maintenance. Changes in user requirements are often translated directly to the level of code, divorcing i t from the high level design it was based on. After a few such changes, the programs may not correspond to any formal high-ievel design, making subsequent maintenance difficult. We argue that maintenance must be based on the knowledge used in synthesizing the high-level design. This requires a development environment where the knowledge about high-level designs is formally represented, and raises the question about how this knowledge will be acquired by the support environment in the first place. In this paper, we present a model that enables the support environment to acquire design knowledge through "learning by observation" of a designer engaged in specifying a high-level design. The knowledge that the learning system begins with is a generic object for expressing design decisions. Based on the input provided by the designer, and a limited interactive querying process, it constructs and continuously refines a taxonomic classification of appiication-specific knowledge and rules a t an appropriate level of generality that capture the rationale of the design. This knowledge can be used subsequently for maintaining system designs and recognizing design situations similar to the ones it has knowledge about.
Information Systems and Different Domain, Functionalities and Types: A Conceptual Study
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Information Systems is one of the important name in the field of Management and Technology. Information Systems is actually combination of some sub systems and dedicated to so many Information Activities; such as collection, selection, organization and processing of information. Information Systems is valuable entity in today's information age. Information Systems is most important and valuable tool for al most all type of organization and institution. Information Systems and its scientific development, today brings so many area and dimension to it; now we may see many types of Information Systems for several organization and domain and functionalities. This paper is comes with main aim and objective of Information Systems; find out latest about several types, domain based types as generally we treat Information Systems as a same entity rather than its versatility.
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The paper explores, in a semiotics approach, the natures and the relationships between the category of information and its relatives that are data and knowledge. The resultant process model makes clear both the evolutionary natures and the triadic relation among the information categories. In addition, drawn on Peirce’s theory of inquiry which stresses the role of community along the inquiring process, a central thesis of the paper is the pragmatic model of information formulation in the information systems field.
Concept and Design of Information and Information Systems
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Systems engineering is developing rapidly, while new standards are created and new tools are being developed. However, the theoretical understanding and the conceptual foundation of systems engineering are still in their early stages. For example, although real-world systems exhibit considerable differences, there is very little distinction in the literature between the system type and the description of its actual system engineering pursuit. We suggest here a new approach to systems engineering. It is based on the premise that the actual process of systems engineering must be adaptive to the real system type. Using this concept, we present a two-dimensional (2-D) taxonomy in which systems are classified according to four levels of technological uncertainty, and three levels of system scope. We then describe the differences found in systems engineering styles in various areas, such as system requirements, functional allocation, systems design, project organization, and management style. We also claim that adapting the wrong system and management style may cause major difficulties during the process of system creation. Two examples will be analyzed to illustrate this point: the famous Space Shuttle case and one of the system development projects we studied.