Clyde W. Holsapple

A Generalized Decision Support System Using Predicate Calculus and Network Data Base Management

In view of the growing prominence of corporate modeling, an important area of research concerns techniques for facilitating the design and utilization of models. In this paper we show how first-order predicate calculus can be used as a language for formally stating modeling knowledge. Furthermore, knowledge stated in this manner can be subjected to the resolution principle. The result is that application specific modeling knowledge need not be embedded in a computer program. Rather, it can be stored in a data base and utilized as needed by a problem processing system employing resolution techniques. Advantages of a decision support system taking an approach of this sort are considerable modeling flexibility, capacity for automating the model formulation and execution processes, and compatibility with a high-level user interface language.

The DSS development system

As decision support systems become more commonplace, the demand for automatic and semiautomatic DSS development systems increases proportionately. Such systems provide a set of tools that guide the construction of models in response to a users query. This paper describes a set of such tools that provide capabilities for analysis, design, module management, and report and graphics generation.

The integration of network data base management and problem resolution

Abstract The value and nature of a mixed approach to knowledge representation is examined. The mixed approach utilizes data base techniques and expressions in first-order predicate calculus. The nature of a general problem processor that can use information organized according to the mixed approach is discussed and illustrated. The users language interface with this problem processor is non-procedural and English-like. The utilization of predicate calculus axioms for data integrity and program module management is also explored.

A consulting system for data base design

Abstract This paper proposes a methodology for automated data base design based on a set of managerial reports. We introduce the notion of report schemata, specified in terms of record types and binary relations, as a framework for analyzing report structures and interactions among reports. Four classes of binary relations are identified, each being characteristic of a certain type of elementary report. Multiple binary relations are used to specify more complex reports. We also discuss concepts such as report covering and the maximal members for transforming reports to a parsimonious representation for data base design. Finally, we formulate this report-driven data base design methodology as an expert consulting system in Artificial Intelligence.

A conceptual framework for studying complex decision processes

Decision making stituations, particularly those involving societal issues, can pose very complex problems for practitioners and investigators from a broad spectrum of disciplines. In this paper, we present theoretical constructs and an organized general framework for studying such complex decision processes. The postulated facets are used in the construction of several hypotheses. Empirical evidence is presented which supports the hypotheses and argues for the viability of the proposed conceptual framework.

The environment approach to decision support

Decision support depends on the effective management of several types of knowledge [2]. Beyond the descriptive knowledge (i.e., data, information) with which management information systems are concerned, decision support systems are also able to manage procedural knowledge. A procedure, also called a model, specifies an algorithm that tells how to derive new knowledge in the sense of facts, expectations, or beliefs. Thus a decision support system (DSS) normally has a knowledge system capable of holding both data and models. The DSS’s software is a problem processing system that can manipulate the knowledge system contents in response to user requests. These requests must be valid statements in the DSS’s language system which defines the syntax, grammar, and semantics of a language that the problem processor can understand. The generic DSS structure is illustrated in Figure 1.

Guidelines for DBMS Selection

Data base management systems are important tools that can be used by application developers to build high quality application systems. The degree of productivity afforded by a particular DBMS depends on the extent to which it is able to satisfy a developer’s needs. This article identifies the major data handling needs of an application developer as implied by the needs of an application system’s end user. The result is a set of criteria for guiding the selection of a DBMS. Each criterion is examined with respect to existing data base management facilities.

FRAMEWORKS FOR ORGANIZATIONAL INFORMATION PROCESSING AND DECISION MAKING

Publisher Summary This chapter discusses the division of information-processing labor within an organization and abilities required of a decision maker. It presents two conceptual frameworks for information processing and decision making. These frameworks provide a common thread with which the various aspects of decision support may be joined, thereby offering a more complete picture of this field. The frameworks allow introducing various computer-based techniques into a human–machine information-processing view. Without the perspective that the frameworks afford, the techniques look like the backside of a tapestry, that is, they are analogous to many strands that appear to be related in some way, but the nature of that relationship and the pattern it produces are not discernible. The frameworks permit to view the front as well, to see not only behind-the-scenes mechanics but also the nature of relationships among the mechanics and the patterns produced. The frameworks furnish a basis for comparative study of various human–machine systems, which differ according to the aspects accomplished by humans and those accomplished by machines. It also discusses the methods for operationalizing the concepts involved in these frameworks.

Data structuring with virtual sets

Abstract Two new data structuring constructs for DBTG-oriented data base systems are introduced: the virtual set and the virtual N : M set. These constructs necessitate few changes in a DDL and require no additional DML commands. Without causing increases in storage and processing costs, these constructs reduce programming effort, enhance the degree of data independence, and offer greater flexibility from the standpoints of subschema and privacy specifications.