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| Content Provider | ACM Digital Library |
|---|---|
| Author | Khan, Arijit Roy, Pratanu Alonso, Gustavo |
| Abstract | Approximated algorithms are often used to estimate the frequency of items on high volume, fast data streams. The most common ones are variations of Count-Min sketch, which use sub-linear space for the count, but can produce errors in the counts of the most frequent items and can misclassify low-frequency items. In this paper, we improve the accuracy of sketch-based algorithms by increasing the frequency estimation accuracy of the most frequent items and reducing the possible misclassification of low-frequency items, while also improving the overall throughput. Our solution, called Augmented Sketch (ASketch), is based on a pre-filtering stage that dynamically identifies and aggregates the most frequent items. Items overflowing the pre-filtering stage are processed using a conventional sketch algorithm, thereby making the solution general and applicable in a wide range of contexts. The pre-filtering stage can be efficiently implemented with SIMD instructions on multi-core machines and can be further parallelized through pipeline parallelism where the filtering stage runs in one core and the sketch algorithm runs in another core. |
| Starting Page | 1449 |
| Ending Page | 1463 |
| Page Count | 15 |
| File Format | |
| ISBN | 9781450335317 |
| DOI | 10.1145/2882903.2882948 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-06-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Approximated algorithms Stream summary Data structures Sketch Data streams |
| Content Type | Text |
| Resource Type | Article |
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