This paper deals with the history and definitions common to data-base technology It delimits the objectives of data-base management systems, discusses important concepts, and defines terminology for use by other papers in this issue, traces the development of data-base systems methodology, gives a uniform example, and presents some trends and issues

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Evolution of Data-Base Management Systems

An important consideration in the design of programming systems for the management of large files of data is the method of treating hierarchical data (that is, data among which logical relationships exist at more than two levels). Recent systems have accomplished this by simply duplicating some essential item of data at several levels. Such duplication makes the storage of even small data bases inefficient; for large masses of data, storage becomes economically unfeasible. Other systems have provided the means to specify and construct hierarchies, but none have provided language that affords control over retrieval and output levels, and control over the scope of output.Within the Time-Shared Data Management System (TDMS), currently being produced at System Development Corporation, a method has been devised for maintaining hierarchical associations within logical entries of a data base. Basically, the technique permits the automatic association of related data through a device known as a repeating group. The term “repeating group” is not new, but the TDMS treatment of the repeating group concept is. This paper describes how this technique is implemented in the language and tables of TDMS.

Treating hierarchical data structures in the SDC Time-Shared Data Management System (TDMS)

General systems behavior theory is concerned with seven levels of living systems-cell, organ, organism, group, organization, society, and supranational system. The following article is an exposition of the basic concepts in this integrative theoretical approach. It is a condensation of a more detailed statement (Miller, 1965a; 1965b; 1965c). The second article is an analysis in terms of this conceptual system of present knowledge concerning one level of living system-the organism. In order to emphasize the cross-level formal identities among levels of living systems, a major consideration of general behavior systems theory, this article follows exactly the same outline, with identical subheadings and section numbers, as other articles written by the author. These deal with the lower levels of living systems-cell and organ (Miller, 1971a)-as well as higher levels-group (Miller, 1971b), organization (Miller, 1972), society, and supranational system. (All the articles will be published together as chapters o...

Living Systems: The Organism

Information Storage and Retrieval (IS&R) encompasses a broad scope of topics ranging from basic techniques for accessing data to sophisticated approaches for the analysis of natural language text and the deduction of information. Within the field, three general areas of investigation can be distinguished not only by their subject matter but also by the types of individuals presently interested in them:(1) Document retrieval,(2) Generalized data management, and(3) Question-answering.A functional description which applies to each of the three areas is presented together with a survey of work being conducted. The similarities and differences of the three areas of IS&R are described. Typical systems which incorporate many of the functions and techniques are described in the appendix.

Information storage and retrieval: a survey and functional description

Stochastic Models for Learning

Probabilistic formulations have been used to design both mathematical models for the behavior of living organisms and machines which display some of the properties of living organisms. Examples of the former include the statistical learning theory of Estes (1950) and the stochastic learning model of Bush and Mosteller (1951). Examples of the latter include the conditional probability machines of Uttley (1956) and the Perceptron of Rosenblatt (1958). Additional references may be found in the reviews by Estes (1959), Gilstrap and Lee (1960), and Minsky (1961). Since these uses of the probabilistic approach apply to either purpose individually, the same approach may be useful in combining purposes, using the machine to learn more about the behavior of living organisms and the principles of learning theory to extend the capabilities of the machine.

Statistical Learning Models for Behavior of an Artificial Organism

At present there are numerous report program generators, most of which are quite similar in purpose and in method of operation. Although they generally relieve the programmer from concern with detailed program logic, they nevertheless require him to have intimate knowledge of the characteristics of his data base, the kinds of entries he wants extracted, the manipulations to be performed, and the detailed format of the desired report. Furthermore, as report program generators have become more generalized (and thus more flexible), they have also become more complex, requiring the user to furnish complicated specification statements in coded form. This, of course, demands an increased amount of training and experience on the part of the programmer, and makes his source programs more subject to error.

Philip R. Bartram

Papers

Statistical Learning Models for Behavior of an Artificial Organism

COMPOSE/PRODUCE: a user-oriented report generator capability within the SDC time-shared data management system

Compose/Produce a User-Oriented Report Generator for a Time-Shared System