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HeatPipe: High Throughput, Low Latency Big Data Heatmap with Spark Streaming

Abstract : Heatmap visualization is a well-known type of visual-ization to alleviate the overplot problem of point visualiza-tion. As such, it is well suited to visualize Big Data. In order to tackle the velocity problem of Big Data, one has to leverage streaming computations. Recently, canopy clustering was shown to be well suited for Big Data heatmap visualization. In this article, we present how to design a streaming algorithm to compute canopy clustering using Apache Spark. This result is directly applicable to be included into a lambda architecture.
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Contributor : Perrot Alexandre <>
Submitted on : Tuesday, July 18, 2017 - 4:21:50 PM
Last modification on : Friday, January 15, 2021 - 11:14:10 AM
Long-term archiving on: : Saturday, January 27, 2018 - 7:40:17 AM


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  • HAL Id : hal-01516888, version 1


Alexandre Perrot, Romain Bourqui, Nicolas Hanusse, David Auber. HeatPipe: High Throughput, Low Latency Big Data Heatmap with Spark Streaming. IV2017 - 21st International Conference on Information Visualisation, Jul 2017, Londres, United Kingdom. ⟨hal-01516888⟩



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