نتایج جستجو برای: streaming sampling

تعداد نتایج: 232078  

2007
Michael Hoffmann S. Muthukrishnan Rajeev Raman

We propose two new data stream models: the reset model and the delta model, motivated by applications to databases, and to tracking the location of spatial points. We present algorithms for several problems that fit within the stream constraint of polylogarithmic space and time. These include tracking the “extent” of the points and Lp sampling.

2012
SHUMO CHU JAMES CHENG

Triangle listing is one of the fundamental algorithmic problems whose solution has numerous applications especially in the analysis of complex networks, such as the computation of clustering coefficients, transitivity, triangular connectivity, trusses, etc. Existing algorithms for triangle listing are mainly in-memory algorithms, whose performance cannot scale with the massive volume of today’s...

2006
André L.L. de Aquino Carlos M.S. Figueiredo Eduardo F. Nakamura Luciana S. Buriol Otávio Fernandes Claudionor J.N. Coelho

— This work presents two data stream algorithms for wireless sensor networks (WSNs), based in sample and sketch technics. For each case, we show that by using our algorithms, we can save energy and reduce delay in WSN applications in different scenarios. Speci cally, the sampling solution, provides a sample of only log n items to represent the original data of n elements. Despite of reduction, ...

2018
Rohan Khade George Mason Jessica Lin Nital Patel

Contrast set mining identifies patterns in the data that can best distinguish between groups. Most of the existing work focuses on categorical and batch data, and they do not scale well for large datasets. In this work, we focus on finding contrast patterns for mixed (quantitative and categorical) and streaming data. We adapt a discretization methodology, Supervised Dynamic and Adaptive Discret...

2017
Jean-Baptiste Tristan Michael L. Wick Joseph Tassarotti

Recent developments in inference algorithms based on stochastic Expectationmaximization or stochastic cellular automata (SCA) have made it possible to employ a variety of randomized data structures that are unavailable to the dominant inference methods in the Bayesian toolkit, including collapsed Gibbs sampling and stochastic variational inference (SVI). Equipped with this recent capability, we...

2016
Ajay Kumar Tanwani Sylvain Calinon

Adapting statistical learning models online with large scale streaming data is a challenging problem. Bayesian non-parametric mixture models provide flexibility in model selection, however, their widespread use is limited by the computational overhead of existing sampling-based and variational techniques for inference. This paper analyses the online inference problem in Bayesian non-parametricm...

2013
P. EPSIBA S. SUBATHRA N. SARDAR BASHA G. SURESH N. KUMARATHARAN

Today’s revolution is mobile communication revolution, which enables us to connect each other worldwide. Due to the enhancement of multimedia and video surveillance services people are more sophisticated. Because of bandwidth requirements and resolution mismatch the designers still striving to provide robust coding technique. In mobile, web applications scalable video coding plays a vital role ...

1998
E. Branchini

We present a self–consistent nonparametric model of the cosmic velocity field based on the spatial distribution of IRAS galaxies in the recently completed all–sky PSCz redshift survey. The dense sampling of PSCz galaxies allows us to infer peculiar velocities field up to large distances with unprecedented high resolution. The most streaking feature of the PSCz model velocity field is a coherent...

2012
Mário Cordeiro

Today streaming text mining plays an important role within real-time social media mining. Given the amount and cadence of the data generated by those platforms, classical text mining techniques are not suitable to deal with such new mining challenges. Event detection is no exception, available algorithms rely on text mining techniques applied to pre-known datasets processed with no restrictions...

Journal: :CoRR 2007
Vladimir Braverman Rafail Ostrovsky Carlo Zaniolo

A streaming model is one where data items arrive over long period of time, either one item at a time or in bursts. Typical tasks include computing various statistics over a sliding window of some fixed time horizon. What makes the streaming model interesting is that as the time progresses, old items expire and new ones arrive. One of the simplest and most central tasks in this model is sampling...

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