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Je-Hao Wang

dblp:70/6169 · 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

Software engineering, systems software and programming languages · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 100%
Databases, data mining, and information retrieval
1 paper
Query processing and optimization · 100%

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

TopicWeightPapersLastEvidence papers
Storage systems
file systems
0.011987
On Multiple Random Accesses and Physical Data Placement in Dynamic Files · IEEE Trans. Software Eng. 1987
Storage systems › indexing
linear hashing
0.011987
On Multiple Random Accesses and Physical Data Placement in Dynamic Files · IEEE Trans. Software Eng. 1987

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

disk access modeling · 0.0
YearPublicationVenuePosition
1987 On Multiple Random Accesses and Physical Data Placement in Dynamic Files
abstract
In the study of data storage and retrieval involving secondary storage devices, for example, magnetic disks, a simplified model of storage that is often used is that each access takes a constant amount of time. However, if some information about the accesses is known, the model should take into consideration the inherent characteristics of the storage devices. In this paper, we assume a more refined model of storage that takes into consideration the seek time, the latency time, and the transmission time of disk accesses separately. We analyze the time required to randomly access a set of records residing on a set of consecutive cylinders on a magnetic disk a number of times, say n, for n ≥ 1. This problem may arise, for example, in the processing of queries that involve several relations in a relational database system. We also analyze the more general situations in which the n operations may represent retrievals, insertions, or deletions, or a combination of them. We assume that the dynamic file structure linear hashing is used for locating and organizing the records. A linear hashing file does not employ any directory and its primary data buckets are assumed to be contiguous, therefore the data area of a linear hashing file corresponds closely to the disk space on a set of consecutive cylinders.
Je-Hao Wang, Tak-Sun Yuen, David Hung-Chang Du
IEEE Trans. Software Eng.1