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Conference proceeding
Parallel Space-Time Kernel Density Estimation
2017 46th International Conference on Parallel Processing (ICPP), pp 483-492
Aug 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The exponential growth of available data has increased the need for interactive exploratory analysis. Dataset can no longer be understood through manual crawling and simple statistics. In Geographical Information Systems (GIS), the dataset is often composed of events localized in space and time; and visualizing such a dataset involves building a map of where the events occurred.We focus in this paper on events that are localized among three dimensions (latitude, longitude, and time), and on computing the first step of the visualization pipeline, space-time kernel density estimation (STKDE), which is most computationally expensive. Starting from a gold standard implementation, we show how algorithm design and engineering, parallel decomposition, and scheduling can be applied to bring near real-time computing to space-time kernel density estimation. We validate our techniques on real world datasets extracted from infectious disease, social media, and ornithology.
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Details
- Title
- Parallel Space-Time Kernel Density Estimation
- Creators
- Erik Saule - Dept. of Comput. Sci., UNC Charlotte, Charlotte, NC, USADinesh Panchananam - Dept. of Comput. Sci., UNC Charlotte, Charlotte, NC, USAAlexander Hohl - University of North Carolina at CharlotteWenwu Tang - University of North Carolina at CharlotteEric Delmelle - University of North Carolina at Charlotte
- Publication Details
- 2017 46th International Conference on Parallel Processing (ICPP), pp 483-492
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Urban Health Collaborative
- Web of Science ID
- WOS:000426952300049
- Scopus ID
- 2-s2.0-85030623566
- Other Identifier
- 991021874427404721
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- Computer Science, Hardware & Architecture