Organization and maintenance of an index for a dynamic random access file is considered. It is assumed that the index must be kept on some pseudo random access backup store like a disc or a drum. The index organization described allows retrieval, insertion, and deletion of keys in time proportional to logk I where I is the size of the index and k is a device dependent natural number such that the performance of the scheme becomes near optimal. Storage utilization is at least 50% but generally much higher. The pages of the index are organized in a special datastructure, so-called B-trees. The scheme is analyzed, performance bounds are obtained, and a near optimal k is computed. Experiments have been performed with indexes up to 100000 keys. An index of size 15000 (100000) can be maintained with an average of 9 (at least 4) transactions per second on an IBM 360/44 with a 2311 disc.

Organization and maintenance of large ordered indexes

Organization and maintenance of an index for a dynamic random access file is considered. It is assumed that the index must be kept on some pseudo random access backup store like a disc or a drum. The index organization described allows retrieval, insertion, and deletion of keys in time proportional to logk I where I is the size of the index and k is a device dependent natural number such that the performance of the scheme becomes near optimal. Storage utilization is at least 50% but generally much higher. The pages of the index are organized in a special data-structure, so-called B-trees. The scheme is analyzed, performance bounds are obtained, and a near optimal k is computed. Experiments have been performed with indices up to 100,000 keys. An index of size 15,000 (100,000) can be maintained with an average of 9 (at least 4) transactions per second on an IBM 360/44 with a 2311 disc.

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Organization and maintenance of large ordered indices

Organization and maintenance of an index for a dynamic random access file is considered. It is assumed that the index must be kept on some pseudo random access backup store like a disc or a drum. The index organization described allows retrieval, insertion, and deletion of keys in time proportional to logk I where I is the size of the index and k is a device dependent natural number such that the performance of the scheme becomes near optimal. Storage utilization is at least 50% but generally much higher. The pages of the index are organized in a special data-structure, so-called B-trees. The scheme is analyzed, performance bounds are obtained, and a near optimal k is computed. Experiments have been performed with indexes up to 100000 keys. An index of size 15000 (100000) can be maintained with an average of 9 (at least 4) transactions per second on an IBM 360/44 with a 2311 disc.

A class of binary trees is described for maintaining ordered sets of data. Random insertions, deletions, and retrievals of keys can be done in time proportional to log N where N is the cardinality of the data-set. Binary B-trees are a modification of B-trees described previously by Bayer and McCreight. They avoid the storage overhead encountered with B-trees and are suitable for processing in a one-level store.

Binary B-trees for virtual memory

An important functional component of ever.y file organization is the set of search mechanisms which are used to locate individually identified records during the process of either update or retrieval. There have been a large number of computer-related search techniques developed in the past 20 years. The objective of this article is to synthesize many of these techniques into one parametrically describable search mechanism. This is accomplished by separating search techniques into two categries: addressing techniques, and tree searching techniques. Significant literature is surveyed for each category. The new concept of a TRIE-TREE search mechanism is then introduced, and used to combine these categories via a generalized model. The model has proven to be a pedagogic convenience in explaining the relation between alternative search techniques. It has also been used irk a practical application as a schema for alternative search strategies which are automatically evaluated during the process of a file organization design. INTRODUCTION Two Primary Areas of Application This paper discusses a number of important techniques which are used to retrieve individually identified data records from a computer memory space. Such techniques find two notable application areas; specifically, the areas of compiler construction and of data base management. When designing a compiler or an assem-bler, one is necessarily concerned with maintaining a symbol table to describe variables encountered as a source program is processed. The records stored in a symbol table contain such data items as a variable's name, type storage address, and domain of definition. Once inserted into the table, a record is consulted whenever the same symbolic name is reencountered in the program. Typically, symbol tables are relatively small, containing something on the order of 100 to 1000 entries, each approximately 10 to 50 characters in length. These tables are generally stored and manipulated internally in main memory where access time is measured in terms of nanoseconds. Because of this speed of data access, a designer's primary objective in selecting a symbol table search strategy is to minimize the average number of machine instructions executed in the process of inserting or retrieving a table entry. There are a nmnber of similarities between this problem and the retrieval of records from a commercial data base. As in a symbol table, the records of a data base correspond to descriptions of logical entities (employees, machine parts, airline flights etc.). Properties of these entities are described by data fields in …

Identifier Search Mechanisms: A Survey and Generalized Model

To translate descriptors into memory locations a memory organization scheme called the balanced tree is introduced, The descriptors that describe the information to be stored or retrieved constitute quasi-inputs to the tree while the outputs are lists on which the information identified by the descriptors is stored. The balanced tree thus provides a stratagem to effect a fast information retrieval with a limited amount of serialized scanning. The algorithms for storing in and retrieving from the balanced tree are outlined. While in a randomly growing tree, the shape of the tree depends on the order of the input, the balanced tree is independent of this order. The expected number of rearrangement steps to keep the tree balanced was derived from combinatorial considerations. Numerical results were obtained by machine computations and are presented in this paper.

Walter I. Landauer

Papers

The Balanced Tree and Its Utilization in Information Retrieval