A database design methodology is defined for the design of large relational databases. First, the data requirements are conceptualized using an extended entity-relationship model, with the extensions being additional semantics such as ternary relationships, optional relationships, and the generalization abstraction. The extended entityrelationship model is then decomposed according to a set of basic entity-relationship constructs, and these are transformed into candidate relations. A set of basic transformations has been developed for the three types of relations: entity relations, extended entity relations, and relationship relations. Candidate relations are further analyzed and modified to attain the highest degree of normalization desired. The methodology produces database designs that are not only accurate representations of reality, but flexible enough to accommodate future processing requirements. It also reduces the number of data dependencies that must be analyzed, using the extended ER model conceptualization, and maintains data integrity through normalization. This approach can be implemented manually or in a simple software package as long as a "good" solution is acceptable and absolute optimality is not required.
New software engineering techniques and the necessity to improve the user interface in increasingly interactive software environments have led to a change in traditional software development methods. Methodologies for improvement of the interface design, an overview of the human factors element, and cost/benefit aspects are explored.
Entity-relationship clustering promotes the simplicity that is vital for fast end-user comprehension, as well as the complexity at a more detailed level to satisfy the database designer's need for extended semantic expression in the conceptual model.
This paper appears in the March, 1972, issue of the Communications of the ACM. Its abstract is reproduced below.Five well-known scheduling policies for movable head disks are compared using the performance criteria of expected seek time (system oriented) and expected waiting time (individual I/O request oriented). Both analytical and simulation results are obtained. The variance of waiting time is introduced as another meaningful measure of performance, showing possible discrimination against individual requests. Then the choice of a utility function to measure total performance including system oriented and individual request oriented measures is described. Such a function allows one to differentiate among the scheduling policies over a wide range of input loading conditions. The selection and implementation of a maximum performance two-policy algorithm are discussed.(Pages 115 through 121 omitted)
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The goal of on-line analytical processing (OLAP) is to quickly answer queries from large amounts of data residing in a data warehouse. Materialized view selection is an optimization problem encountered in OLAP systems. Published work on the problem of materialized view selection presents solutions scalable in the number of possible views. However, the number of possible views is exponential relative to the number of database dimensions. A truly scalable solution must be polynomial time relative to the number of dimensions. We present such a solution, our Polynomial Greedy Algorithm. Complexity analysis proves scalability, and a performance study verifies the result. Empirical evidence demonstrates benefits close to existing algorithms. We conclude the Polynomial Greedy Algorithm functions effectively where existing algorithms fail dramatically.
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