The hardware required for efficient text retrieval differs from that required for retrieval of formatted data. Here is an examination of such hardware, particularly term comparators.

Text Retrieval Computers

Publisher Summary The chapter introduces the 90-10 rule that characterizes the problems of contemporary database systems. The chapter presents the direction toward hardware solutions to software database systems problems. The 9-10 rule in the context of a contemporary database system with very large databases is examined. A contemporary database system resides in a general-purpose host computer and consists of four major software components: the application programs, the operating system, the database management software, and on-line I/O routines. One solution is to upgrade the present host computer with a higher performance computer. With faster CPUs, larger memory, greater channel capacity, and additional controllers, the new host computer may overcome the bottlenecks created by the 90-10 rule. The chapter explains that the progress being made on two fronts are advances in technology and advances in research. Databases fall into two broad categories: those with limited formats and those with elaborate formats. The chapter discusses that there are three prevailing database models—that is, hierarchical, CODASYL, and relational that underscore the formats and data sublanguages of the contemporary databases. The chapter concludes that high-performance database computers allow the growth of the existing databases, open up new database management applications, and provide new features such as security and clustering hardware mechanisms that are not presently found in software-based environments.

Data base computers

This paper discusses the hardware design of a term detection unit which may be used in the scanning of text emanating from a serial source such as disk or bubble memory. The main objective of this design is the implementation of a high performance unit which can detect any one of many terms (e.g., 1024 terms) while accepting source text at disk transfer rates. The unit incorporates "off-the-shelf" currently available chips. The design involves a hardware-based hashing scheme that allows incoming text to be compared to selected terms in a RAM which contains all of the strings to be detected. The organization of data in the RAM of the term detector is dependent on a graph-theoretic algorithm which computes maximal matchings on bipartite graphs. The capability of the unit depends on various parameters in the design, and this dependence is demonstrated by means of various tables that report on the results of various simulation studies.

A Hardware Hashing Scheme in the Design of a Multiterm String Comparator

An improved method is disclosed for operating an interactive text processing system to integrate and process DP (data processing) type structured field data and WP (word or text processing) type text or string field data in the same record The method involves displaying to the text processing system operator a menu of data type codes selectable through a keyboard for defining the types of data fields in a record In response to operator selected field types, structured fields of fixed length and address pointers of fixed length for the string fields are established for the records When the data is keyed in, the structured fields are concatenated with the address pointers for the string fields, and the text data, including format control codes, is stored at a system selected address on an auxiliary storage device such as a disk file The structured field data is displayed or printed under control of the field length definitions and the string field data is displayed or printed under control of the address pointer and imbedded format control codes

A method for integrating structured data and string data on a text processing system

The disclosure relates to an improvement to an information processing system for conducting data processing operations wherein the processing relates to the value of the information and conducting text processing operations wherein the processing affects only the format of information. The improvement provides the combination of means for initiating a data processing session in said system, means for switching to a text processing session before the completion of the data processing session, means for storing the status of the non-completed data processing session during the text processing session, and means for switching back from the text processing session to the non-completed data processing session.

A text processor having an interacitve display terminal which alternately functions as a data processing terminal

Many people have suggested adding scanning logic to a rotating memory system, such as disk or shift registers, to allow faster execution of database operations. Most of these have been concerned with producing a form of associative memory which is then used to implement one or more of the models for information storage and retrieval, such as relational or hierarchical. While these are capable of searching for simple character strings, they are incapable of handling the complex patterns sometimes necessary for textual information retrieval. In addition, textual information retrieval does not lend itself to highly formatted databases, encoding of information, or arbitrary ordering of data, concepts common to the other structures.A description of the operations desirable in textual information retrieval is given, and contrasted to those operations allowed in non-textual systems. The general structure for a scanning processor is presented, and a number of different trade-offs in its design and operation are discussed.

Lee A. Hollaar

Papers

Rotating memory processors for the matching of complex textual patterns