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Sampling Massive Streaming Call Graphs by Shazia Tabassum; João Gama is a scholarly article available to read on EtoBox.

What is Sampling Massive Streaming Call Graphs about?

The problem of analyzing massive graph streams in real time is growing along with the size of streams. Sampling techniques have been used to analyze these streams in real time. However, it is difficult to answer questions like, which structures are well preserved by the sampling techniques over the evolution of streams? Which sampling techniques yield proper estimates for directed and weighted graphs? Which techniques have least time complexity etc? In this work, we have answered the above questions by comparing and analyzing the evolutionary samples of such graph streams. We have evaluated sequential sampling techniques by comparing the structural metrics from their samples. We have also presented a biased version of reservoir sampling, which shows better comparative results in our scenario. We have carried out rigorous experiments over a massive stream of 3 hundred million calls made by 11 million anonymous subscribers over 31 days. We evaluated node based and edge based methods of sampling. We have compared the samples generated by using sequential algorithms like, space saving algorithm for finding topK items, reservoir sampling, and a biased version of reservoir sampling. Our

Author

Shazia Tabassum; João Gama

Publisher

ACM

Published

2016

Language

EN