Whether to batch-process data or to randomly update files in real-time is a common design choice faced by information system analysts. A related (but different) problem is whether or not decisions should be batch processed. Though this latter problem is undoubtedly of greater managerial concern it has received less attention. This paper discusses considerations relevant to the optimal batching and timing of decisions and illustrates these by examples. A simple illustrative mathematical model of the problem is developed and optimized. This topic is of interest to MIS designers since their systems may include automated decision making and always support the decision making process.

The literature on physical database design in general, and on file segmentation in particular, typically ignores any consideration of data security and the cost to enforce it. If records can be physically designed so that all data elements in a given record type have identical security restrictions for a given user, then data element level security enforcement can be transformed into the less costly file level security enforcement for that user and rie. Similarly, if all record types have identical security restrictions, file based security might be sufficient. This paper extends an earlier model for file segmentation to include security considerations. The extended model embeds the security measures into the logical file structure and exploits a four category taxonomy of security restriction types. The model is used to generalize the interaction between element level selective security and physical database design.

Various procedures have been developed to select efficient storage structures; all of these procedures, however, require certain statistics to describe the use of attributes by problem programs. A very practical problem faced by anyone attempting to use any of these selection techniques is the determination of values for the attribute use statistics. This paper reports on one method which has been empirically tested for determination of attribute access probabilities. Central to this method is recognition of five kinds of statistic collection biases which are systematically considered by the proposed method but which do confound collection by previously developed methods. Finally, experimental results are reported which indicate specific comparative advantages of the proposed method over ones used by the DP staff of a medium-sized manufacturer.

The physical structure and relative placement of information elements within a data base is critical for the efficient design of a computerized information system which is shared by a community of users. Traditionally the selection among alternative structural designs has been handled largely via heuristics. Recent research has shown that a number of significant design problems can be stated mathematically as nonlinear, integer, zero-one programming problems. In concept, therefore, mathematical programming algorithms can be used to determine "optimal" data base designs. In practice, one finds that realistic problems of even modest size are computationally infeasible. This paper presents a means for overcoming this difficulty in the design of data base records. A metric with which to measure the similarity of usage among data items is developed and used by a clustering algorithm to reduce the space of alternative designs to a point where solution is economically feasible.