Performance Parameter Measurements of Generic Files

This study discusses the performance parameter measurements of generic files, the pile file, the sequential file, the indexed-sequential file, the indexed file and the direct file. The file performance measurements are compiled in a software package. The study then describes the use of such software package as a simulation tool in a file design environment

Multilist and inverted file system performance measurements

This study evaluates the multilist and inverted file systems. It describes the structure of the two file system and then proceeds to investigate the performance. The performance is based on quantitative estimates of space requirements for file system, time to retrieve records, time to insert a record, time to delete a record, time to update a record and time to exhaustively read and reorganize the file system. The study then investigates specific situations in which one file system seems to perform better than the other

Input Data Pattern Encoding for Neural Net Algorithms

First, a brief overview of neural networks and their applications are described, including the BAM (Bidirectional Associative Memory) model. A bucket-weight-matrix scheme is proposed, which is a data pattern encoding method that is necessary to transform a set of real-world numbers into neural network state numbers without losing the pattern property the set has. The scheme is designed as a neural net so that it can be combined with other data processing neural nets. The net itself can be used as a bucket-sorting net also. This shows that traditional data structure problems can be an area that neural networks may conquer, too. A simulation of the net combined with the BAM model on a digital computer is done to show performance of the proposed data encoding method with both non-numerical image pattern and numerical data pattern examples

A Definition Optimization Technique Used In A Code Translation Algorithm

Data flow analysis is used to optimize variable definitions in a program that translates microprocessor object code to a higher order language. © 1989, ACM. All rights reserved

Automated Translation of Digital Logic Equations into Optimized VHDL code

It was desired to develop an algorithm for the automated translation of finite slate machines from state table form to optimized VHDL form. To do this, algorithms arc needed for reducing the state machine to simplest form, making state assignments, producing minimal logic equations to represent the state machine, and producing VHDL code which describes the intended circuit. Various such algorithms were examined and a prototype program written to perform this translation

Micro Database Management System Language

There are two approaches to solve computational problems in a microcomputer environment: Non-database approach: uses a high level programming language with non-database files as input and/or output files. Database approach: uses the programming language embedded in the micro Data Base Management System(DBMS), with the database defined by the integrated database definition language as input and/or output files. Adopting the appropriate approach in any single application may save cost and time. This paper compares the two different approaches while solving the same Control Section (CSECT) Interaction Hierarchy problem and suggests which to use when

A direct access method using a neural network model

One of the concerns in computer science involves optimizing usage of machines to make them more efficient and cost effective. One item of particular concern is the use of secondary storage devices, devices that store data other than in the main memory of the computer to which it is attached. The times for searching for data on these devices consistently proves to be a contributing factor in inefficient computer usage. One data access method that avoids searching when possible is the hashing method. A function is defined to return the record number of a record based on its key field. The record can then be read in directly. A problem exists when more than one key maps to the same record number, called a collision, and must be dealt with, usually adding search time in the process. Training a neural network to do this avoids these collisions. The Hamming network, based on the Hamming distances of two binary patterns, is trained to map the key fields directly to the record number of the data. The key must be converted to a binary format. The program passes the key to the network that simultaneously calculates the form of the Hamming distance between that key and all keys known to be in the file. A MAXNET network takes these distances and reduces them until no more than one is positive. The record number is found from the results, and the data can be accessed directly. All disadvantages from the software version are virtually eliminated