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Probabilistic diagnosis of hot spots

Author: Kenneth Salem; Daniel Barbara; Richard J Lipton
Publisher: Princeton, N.J. : Princeton University, Dept. of Computer Science, [1991]
Series: Princeton University.; Department of Computer Science.; Technical report
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:
Abstract: "Commonly, a few objects in a database account for a large share of all database accesses. These objects are called hot spots. The ability to determine which objects are hot spots opens the door to a variety of performance improvements. Data reorganization, migration, and replication techniques can take advantage of knowledge of hot spots to improve performance at low cost. In this paper we present some
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Document Type: Book
All Authors / Contributors: Kenneth Salem; Daniel Barbara; Richard J Lipton
OCLC Number: 24771521
Notes: "June 1991."
Description: 22, [6] pages : illustrations ; 28 cm.
Series Title: Princeton University.; Department of Computer Science.; Technical report
Responsibility: Kenneth Salem, Daniel Barbara, Richard J. Lipton.

Abstract:

Abstract: "Commonly, a few objects in a database account for a large share of all database accesses. These objects are called hot spots. The ability to determine which objects are hot spots opens the door to a variety of performance improvements. Data reorganization, migration, and replication techniques can take advantage of knowledge of hot spots to improve performance at low cost. In this paper we present some techniques that can be used to identify those objects in the database that account for more than a specified percentage of database accesses. Identification is accomplished by analyzing a string of database references and collecting statistics.

Depending on the length of the reference string and the amount of space available for the analysis, each technique will have a non-zero probability of false diagnosis, i.e., mistaking 'cold' items for hot spots and vice versa. We compare the techniques analytically and show the tradeoffs among time, space and the probability of false diagnoses."

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