Abstract
A stream can be thought of as a very large set of data, sometimes even infinite, which arrives sequentially and must be processed without the possibility of being stored. In fact, the memory available to the algorithm is limited and it is not possible to store the whole stream of data which is instead scanned upon arrival and summarized through a succinct data structure in order to maintain only the information of interest. Two of the main tasks related to data stream processing are frequency estimation and heavy hitter detection. The frequency estimation problem requires estimating the frequency of each item, that is the number of times or the weight with which each appears in the stream, while heavy hitter detection means the detection of all those items with a frequency higher than a fixed threshold. In this work we design and analyze ACMSS, an algorithm for frequency estimation and heavy hitter detection, and compare it against the state of the art ASketch algorithm. We show that, given the same budgeted amount of memory, for the task of frequency estimation our algorithm outperforms ASketch with regard to accuracy. Furthermore, we show that, under the assumptions stated by its authors, ASketch may not be able to report all of the heavy hitters whilst ACMSS will provide with high probability the full list of heavy hitters.
Highlights
In the data stream model, data arrives or can be accessed only sequentially and in a given order; no random access to the data is allowed
In this paper we are concerned with the problems of frequency estimation and frequent item detection in data streams
We compare our algorithm to ASKETCH [10], the state of the art algorithm for these problems and, through extensive experimental results, we show that ACMSS achieves better accuracy than ASKETCH for the problem of frequency estimation
Summary
In the data stream model, data arrives or can be accessed only sequentially and in a given order; no random access to the data is allowed. Queries about the data stream are answered using that summary, and the time for processing an item and computing the answer to a given query is limited. Two of the most important and well studied problems in the field of Data Mining are frequency estimation of data stream items and the detection of heavy hitters, known as frequent items. In this paper we are concerned with the problems of frequency estimation and frequent item detection in data streams. We compare our algorithm to ASKETCH [10], the state of the art algorithm for these problems and, through extensive experimental results, we show that ACMSS achieves better accuracy than ASKETCH for the problem of frequency estimation.
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