NDLI: Methods for finding frequent items in data streams

Content Provider	Springer Nature Link
Author	Cormode, Graham Hadjieleftheriou, Marios
Copyright Year	2009
Abstract	The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Many applications rely directly or indirectly on finding the frequent items, and implementations are in use in large scale industrial systems. However, there has not been much comparison of the different methods under uniform experimental conditions. It is common to find papers touching on this topic in which important related work is mischaracterized, overlooked, or reinvented. In this paper, we aim to present the most important algorithms for this problem in a common framework. We have created baseline implementations of the algorithms and used these to perform a thorough experimental study of their properties. We give empirical evidence that there is considerable variation in the performance of frequent items algorithms. The best methods can be implemented to find frequent items with high accuracy using only tens of kilobytes of memory, at rates of millions of items per second on cheap modern hardware.
Starting Page	3
Ending Page	20
Page Count	18
File Format	PDF
ISSN	10668888
Journal	The VLDB Journal
Volume Number	19
Issue Number	1
e-ISSN	0949877X
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-12-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Database Management
Content Type	Text
Resource Type	Article
Subject	Information Systems Hardware and Architecture

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