Constructing fading histograms from data streams

Frequent Pattern Mining from Time-Fading Streams of Uncertain Data

Nowadays, streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding wind...

Workload-Optimal Histograms on Streams

Histograms are used in many ways in conventional databases and in data stream processing for summarizing massive data distributions. Previous work on constructing histograms on data streams with provable guarantees have not taken into account the workload characteristics of databases which show some parts of the distributions to be more frequently used than the others; on the other hand, previo...

Fast Histograms using Adaptive CUDA Streams

Histograms are widely used in medical imaging, network intrusion detection, packet analysis and other streambased high throughput applications. However, while porting such software stacks to the GPU, the computation of the histogram is a typical bottleneck primarily due to the large impact on kernel speed by atomic operations. In this work, we propose a stream-based model implemented in CUDA, u...

Quantiles and Equi-depth Histograms over Streams

Constructing Irregular Histograms by Penalized Likelihood

We propose a fully automatic procedure for the construction of irregular histograms. For a given number of bins, the maximum likelihood histogram is known to be the result of a dynamic programming algorithm. To choose the number of bins, we propose two different penalties motivated by recent work in model selection by Castellan [6] and Massart [26]. We give a complete description of the algorit...