Optimal Aggregation Algorithms for Middleware

Optimal Aggregation Algorithms for Middleware (2001)

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by Ronald Fagin , Amnon Lotem Y , Moni Naor Z

Venue:	In PODS
Citations:	431 - 4 self

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Datum	Value	Source
TITLE	Optimal Aggregation Algorithms for Middleware	INFERENCE
AUTHOR NAME	Ronald Fagin	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM Almaden Research Center	SVM HeaderParse 0.2
AUTHOR ADDR	650 Harry Road, San Jose, California 95120. Email: fagin@almaden.ibm.com	SVM HeaderParse 0.2
AUTHOR NAME	Amnon Lotem Y	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM Almaden Research Center	SVM HeaderParse 0.2
AUTHOR ADDR	650 Harry Road, San Jose, California 95120. Email: fagin@almaden.ibm.com	SVM HeaderParse 0.2
AUTHOR NAME	Moni Naor Z	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM Almaden Research Center	SVM HeaderParse 0.2
AUTHOR ADDR	650 Harry Road, San Jose, California 95120. Email: fagin@almaden.ibm.com	SVM HeaderParse 0.2
ABSTRACT	Abstract: Assume that each object in a database has m grades, or scores, one for each of m attributes. For example, an object can have a color grade, that tells how red it is, and a shape grade, that tells how round it is. For each attribute, there is a sorted list, which lists each object and its grade under that attribute, sorted by grade (highest grade first). There is some monotone aggregation function, orcombining rule, such as min or average, that combines the individual grades to obtain an overall grade. To determine the top k objects (that have the best overall grades), the naive algorithm must access every object in the database, to find its grade under each attribute. Fagin has given an algorithm (“Fagin’s Algorithm”, or FA) that is much more efficient. For some monotone aggregation functions, FA is optimal with high probability in the worst case. We analyze an elegant and remarkably simple algorithm (“the threshold algorithm”, or TA) that is optimal in a much stronger sense than FA. We show that TA is essentially optimal, not just for some monotone aggregation functions, but for all of them, and not just in a high-probability worst-case sense, but over every database. Unlike FA, which requires large buffers (whose size may grow unboundedly as the database size grows), TA requires only a small, constant-size buffer. TA allows early stopping, which yields, in a precise sense, an approximate version of the top k answers.	SVM HeaderParse 0.2
YEAR	2001	INFERENCE
VENUE	In PODS	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	102--113	INFERENCE
VOLUME	66	INFERENCE
CITATIONS	22 found	ParsCit 1.0