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Anil K. Garg

dblp:28/2436 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1986
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Indexing and storage engines · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Indexing and storage engines
file organization
0.011986
Order-Preserving Key Transformations · ACM Trans. Database Syst. 1986
Indexing and storage engines › file organization
hash file organization
0.011986
Order-Preserving Key Transformations · ACM Trans. Database Syst. 1986

Methods — techniques the papers use, named apart from their topics

table-based transformation · 0.0order-preserving transformation · 0.0
YearPublicationVenuePosition
1986 Order-Preserving Key Transformations
abstract
File organizations based on conventional hash functions provide faster access to the stored records in comparison with tree-like file structures. Tree structures such as B + -trees and ISAM do provide for sequential processing, but require considerable storage for the indices. When sequential processing is needed a table that performs an order-preserving transformation on keys can be used. H is an order-preserving key transform if H(K 1 ) ⩾ H(K 2 ), for all keys K 1 > K 2 . We present methodologies for constructing such key transforms, and illustrate them for some real-life key sets. Storage requirements for the table needed to carry out the transformation are less than those needed for the indices.
Anil K. Garg, Calvin C. Gotlieb
ACM Trans. Database Syst.1