Results 61 to 70 of about 935,434 (195)
Efficient multi-label classification for evolving data streams [PDF]
, 2010 Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios.
Bifet, Albert +3 more
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Finding Subcube Heavy Hitters in Analytics Data Streams
, 2018 Data streams typically have items of large number of dimensions. We study the fundamental heavy-hitters problem in this setting. Formally, the data stream consists of $d$-dimensional items $x_1,\ldots,x_m \in [n]^d$.
Kveton, Branislav +3 more
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Clustering categorical data streams
Journal of Computational Methods in Sciences and Engineering, 2011 In this paper, we propose an efficient clustering algorithm for analyzing categorical data streams. It has been proved that the proposed algorithm uses small memory footprints. We provide empirical analysis on the performance of the algorithm in clustering both synthetic and real data streams.
He, Zengyou +3 more
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ACM SIGMOD Record, 2005 The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic.
Gaber, M. +2 more
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Apache Spark SVM for Predicting Obstructive Sleep Apnea
Big Data and Cognitive Computing, 2020 Obstructive sleep apnea (OSA), a common form of sleep apnea generally caused by a collapse of the upper respiratory airway, is associated with one of the leading causes of death in adults: hypertension, cardiovascular and cerebrovascular disease. In this
Katie Jin, Sikha Bagui
doaj +1 more source
rstream: Streams of Random Numbers for Stochastic Simulation [PDF]
, 2005 The package rstream provides a unified interface to streams of random numbers for the R statistical computing language. Features are: * independent streams of random numbers * substreams * easy handling of streams (initialize, reset) * antithetic random ...
L'Ecuyer, Pierre, Leydold, Josef
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2011 Data Compression Conference, 2011 We present a general-purpose, loss less compressor for streaming data. This compressor is based on the deplump probabilistic compressor for batch data. Approximations to the inference procedure used in the probabilistic model underpinning deplump are introduced that yield the computational asyptotics necessary for stream compression. We demonstrate the
Bartlett, N, Wood, F
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Active Fuzzy Weighting Ensemble for Dealing with Concept Drift
International Journal of Computational Intelligence Systems, 2018 The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by.
Fan Dong +3 more
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Entropy, 2020 In the era of a large number of tools and applications that constantly produce massive amounts of data, their processing and proper classification is becoming both increasingly hard and important.
Weronika Wegier, Pawel Ksieniewicz
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GreedyDual-Join: Locality-Aware Buffer Management for Approximate Join Processing Over Data Streams [PDF]
, 1997 We investigate adaptive buffer management techniques for approximate evaluation of sliding window joins over multiple data streams. In many applications, data stream processing systems have limited memory or have to deal with very high speed data streams.
Chang, Ching +3 more
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