Stratified random sampling (SRS) is a fundamental sampling technique that provides accurate estimates for aggregate queries using a small size sample, and has been used widely for approximate query processing. A key question in SRS is how to partition a target sample size among different strata. While Neyman allocation provides a solution that minimizes the variance of an estimate using this sa...

We consider the tasks of testing and spot-checking for data streams. These testers and spotcheckers are potentially useful in real-time or near real-time applications that process huge data sets. Crucial aspects of the computational model include the space complexity of the testers and spot-checkers (ideally much lower than the size of the input stream) and the number of passes that the tester ...

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing — based on the chosen sample size — can make a systematic trade-off between the output accuracy and computation effi...

Abstract : Video streaming demands high data rates and hard de−lay constraints, and it raises several challenges on to−day’s packet−based and best−effort internet. In this paper, we propose a simple multiple−description coding (MDC) technique based on sub−sampling and cubic−spline interpolation to provide spatial diversity, such that no additional buffering delay or storage is required. Analysi...

Dick Bulterman Oratrix Development Valeriusplein, 30 in Amsterdam, The Netherlands E-mail:[email protected] INTRODUCTION The amount of streaming media available on the web is orders of magnitude less than the amount of text and the resources for locating relevant information contained in streaming media [4], such as search engines, web directories and portals are not as well developed ...

We propose a Conditional Density Filtering (C-DF) algorithm for efficient online Bayesian inference. C-DF adapts Gibbs sampling to the online setting, sampling from approximations to conditional posterior distributions obtained by tracking of surrogate conditional sufficient statistics as new data arrive. This tracking eliminates the need to store or process the entire data set simultaneously. ...

An increasing number of scientific applications rely on stream processing for generating timely insights from data feeds of scientific instruments, simulations, and Internet-of-Thing (IoT) sensors. The development of streaming applications is a complex task and requires the integration of heterogeneous, distributed infrastructure, frameworks, middleware and application components. Different app...

This paper examines the problem of real-time estimation of the capacity, which is also known as the bottleneck bandwidth, of a network path using end-to-end measurements in a video streaming application. We utilize two basic packet-pair bandwidth-sampling methods and show how they can be applied in real-time to the video traffic. We then examine the performance of both sampling methods in a num...

We address efficient compression and real-time streaming of stereoscopic video over the current Internet. We first propose content-adaptive stereo video coding (CA-SC), where additional coding gain, over that can be achieved by exploiting only inter-view correlations, is targeted by down-sampling one of the views spatially or temporally depending on the content, based on the well-known theory t...

—Over low bit rate channels, we adopt the streaming of nonlinearly sampled video frames (i.e., key-frame slideshow) synchronized with the audio stream. Given the channel and buffer limits, we wish to obtain a set of sampled frames that is not only feasible (i.e., no frame drop) but also optimal in terms of maximal information flow (given that the semantic information contents of each frame can ...