Stream reduction is the process of removing unwanted elements from a stream of outputs. It is a key component of many GPGPU algorithms, especially in multi-pass algorithms: the stream reduction is used to remove unwanted elements from the output of a previous pass before sending it as input for the next pass. In this paper, we present a new efficient algorithm for stream reduction on the GPU. O...

There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualisation for emergency and disaster management, real-time optimisation for courier pick-up an...

A data stream is a continuous, huge, fast changing, rapid, infinite sequence of data elements. The nature of streaming data makes it essential to use online algorithms which require only one scan over the data for knowledge discovery. In this paper, we propose a new single-pass algorithm, called DSMFI (Data Stream Mining for Frequent Itemsets), to mine all frequent itemsets over the entire hist...

todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. in this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...

The Common Scrambling Algorithm (CSA) is used to encrypt streams of video data in the Digital Video Broadcasting (DVB) system. The algorithm uses a combination of a stream and a block cipher, apparently for a larger security margin. However these two algorithms share a common key. In this paper we present a fault attack on the block cipher which can be launched without regarding the stream ciph...

Stream data is common in many applications, e.g., stock quotes, merchandize sales record, system logs, etc.. It is of great importance to analyze these stream data. As one of the most commonly used techniques, clustering on streams can help to detect and monitor correlations among streams. Due to the unique nature of streaming data, direct application of most existing clustering algorithms fail...

We continue the study of approximating the number of distinct elements in the data stream model to within a (1 ± ) factor with constant probability. It was shown by Indyk and Woodruff (FOCS, 2003) that if the stream may consist of arbitrary data arriving to the streaming algorithm in an arbitrary order, then any 1-pass algorithm requires Ω(1/ ) bits of space to perform this task. In an attempt ...

Access control mechanisms and Privacy Protection Mechanisms (PPM) have been proposed for data streams. The access control for data stream allows roles access to tuples satisfying an authorized predicate sliding-window query. When the sensitive stream data is shared without a PPM the privacy can be compromised. The PPM meets privacy requirements, e.g., k-anonymity or l-diversity by generalizatio...

Monitoring data in coal mine is essentially data stream, and missing coal mine monitoring data is caused by harsh coal mine environment, therefore coal mine safety evaluation can be seen as incomplete labeled data stream classification. The method is proposed for unlabeled data and concept drift in incomplete labeled data stream in this paper that uses semi-supervised learning method based on k...

In this paper we propose a new extension to clustering data streams based on the Temporal Relationship Among Clusters for Data Streams (TRACDS). This is not a new clustering algorithm, but rather a way to capture the temporal relationships among clusters that is inherent in the ordering of observations in the data stream. We propose to capture this ordering relationship among the clusters by ov...