Paper Info
Title
Higher-Accuracy for Identifying Frequent Items over Real-Time Packet Streams
Abstract
In this paper, we classified the synopses data structure into two major types, the Equal Synopses and Unequal Synopses. Usually, a Top-k query is always processed over equal synopses, but Top-k query is very difficult to implement over unequal synopses because of resulting inaccurate approximate answers. Therefore, we present a Dynamic Synopsis which is developed by DSW (Dynamic Sub-Window) algorithm to support the processing of Top-k aggregate queries over unequal synopses and guarantee the accuracy of the approximation results. Our experiment results show that using Dynamic Synopses have significant performance benefits of improving the accuracy of approximation answers on real time traffic analyses over packet streaming networks.
Year
DOI
Venue
2008
10.1007/978-3-540-85930-7_9
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES
Keywords
Field
DocType
sliding window,Top-k,frequent items,dynamic synopses
Data mining,Data structure,Sliding window protocol,Computer science,Network packet,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
15
1865-0929
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Ling Wang1123.92
Yang Koo Lee2448.62
Keun Ho Ryu388385.61