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Introduction --- Applications create what are called 'log files' to keep track of activities taking place at any given time. These files, which are far from being simple text outputs, can be very complex to go through, especially if the server bei
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Building secure systems is difficult. It would be nice if we had a bunch of well-designed crypto building blocks that we could assemble in all sorts of ways and be certain that they would, no matter what, yield a secure system overall. There are, in fact, folks working on such things at a theoretical level…
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CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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.