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William D. Ruchte

dblp:76/5140 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1987
—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%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Indexing and storage engines › file organization
dynamic file organization
0.011987
Linear Hashing with Priority Splitting: A Method for Improving the Retrieval Performance of Linear Hashing · ICDE 1987
Indexing and storage engines
hash index
0.011987
Linear Hashing with Priority Splitting: A Method for Improving the Retrieval Performance of Linear Hashing · ICDE 1987
Indexing and storage engines › hash index › dynamic hashing
linear hashing
0.011987
Linear Hashing with Priority Splitting: A Method for Improving the Retrieval Performance of Linear Hashing · ICDE 1987

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

priority splitting · 0.0overflow page sizing · 0.0
YearPublicationVenuePosition
1987 Linear Hashing with Priority Splitting: A Method for Improving the Retrieval Performance of Linear Hashing
abstract
Linear hashing is a technique for constructing dynamic files for direct access. It has an advantage over other dynamic methods in that it lacks a directory. However, a weakness of the method is that at high packing factors, it requires more probes on the average to access a record than do many of the static methods. This paper presents a straightforward modification of linear hashing which, according to experimental results, significantly reduces the average number of retrieval probes in almost aft cases when compared with standard linear hashing. The parameter of overflow page size is an important one for adjusting performance. By choosing an appropriate overflow page size, the user may obtain results which are also better or comparable to those of other variants of linear hashing. In addition, the paper analyzes the effects of varying the primary page size, the overflow page size, and the packing factor on retrieval performance.
William D. Ruchte, Alan L. Tharp
ICDE1