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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kang, U. Choi, Jihoon Lim, Yongsub |
| Abstract | How can we discover interesting patterns from time-evolving high speed data streams? How to analyze the data streams quickly and accurately, with little space overhead? High speed data stream has been receiving increasing attentions due to its wide applications such as sensors, network traffic, social networks, etc. One of the most fundamental tasks in the data stream is to find frequent items; especially, finding recently frequent items has become important in real world applications. In this paper, we propose TwMinSwap, a fast, accurate, and space-efficient method for tracking recent frequent items. TwMinSwap is a deterministic version of our motivating algorithm TwSample which is a sampling based randomized algorithm with nice theoretical guarantees. TwMinSwap improves TwSample in terms of speed, accuracy, and memory usage. Both require only O(k) memory spaces, and do not require any prior knowledge on the stream such as its length and the number of distinct items in the stream. Through extensive experiments, we demonstrate that TwMinSwap outperforms all competitors in terms of accuracy and memory usage, with fast running time. Thanks to TwMinSwap, we report interesting discoveries in real world data streams, including the difference of trends between the winner and the loser of U.S. presidential candidates, and doubly-active patterns of movies. |
| Starting Page | 1109 |
| Ending Page | 1118 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450325981 |
| DOI | 10.1145/2661829.2662006 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Hot items Data stream Top-k items Frequent items Time-weighted counting Sampling |
| Content Type | Text |
| Resource Type | Article |
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