2014 Proceeding- Editors:
- Varun Chandola,
- Ranga Raju Vatsavai
Big data is emerging as an important area of research for data researchers and scientists. This area has also seen significant interest from the industry and federal agencies alike, as evidenced by the recent White House initiative on "Big data research and development". Within the realm of big data, spatial and spatio-temporal data is one of fastest growing types of data With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in ground, air- and space-borne sensor technologies has led to an unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. Today, analyzing this data poses a massive challenge to researchers.
Proceeding Downloads
A computational framework for ontologically storing and analyzing very large overhead image sets
- Randy C. Brost,
- William C. McLendon,
- Ojas Parekh,
- Mark D. Rintoul,
- David R. Strip,
- Diane Myung-kyung Woodbridge
We describe a computational approach to remote sensing image analysis that addresses many of the classic problems associated with storage, search, and query. This process starts by automatically annotating the fundamental objects in the image data set ...
High performance integrated spatial big data analytics
The growth of spatial big data has been explosive thanks to cost-effective and ubiquitous positioning technologies, and the generation of data from multiple sources in multi-forms. Such emerging spatial data has high potential to create new insights and ...
Haggis: turbocharge a MapReduce based spatial data warehousing system with GPU engine
Spatial query processing involves complex multidimensional objects and compute intensive spatial operations, and therefore requires a high performance approach to meet the rapid data analytics requirements of modern spatial applications. Recently, ...
A computational framework for finding interestingness hotspots in large spatio-temporal grids
Gridded datasets are quite common in scientific computing as many disciplines produce large amounts of samples relying on regular spatial grid-structures that identify locations where measurements are taken. Very large gridded datasets with a lot of ...
Parallel QuadTree encoding of large-scale raster geospatial data on multicore CPUs and GPGPUs
Global remote sensing and large-scale environment modeling have generated vast amounts of raster geospatial images. To gain a better understanding of this data, researchers are interested in performing spatial queries over them, and the computation of ...
Partitioning strategies for spatio-textual similarity join
Given a collection of geo-tagged objects with associated textual descriptors, the spatio-textual similarity join (STJoin) problem is to identify all pairs of similar objects that are close in distance. This task, which is useful in localized ...
A MapReduce algorithm to create contiguity weights for spatial analysis of big data
Spatial analysis of Big data is a key component of Cyber-GIS. However, how to utilize existing cyberinfrastructure (e.g. large computing clusters) to perform parallel and distributed spatial analysis on Big data remains a huge challenge. Problems such ...
An efficient GPU multiple-observer siting method based on sparse-matrix multiplication
- Guilherme C. Pena,
- Salles V. G. Magalhães,
- Marcus V. A. Andrade,
- W. Randolph Franklin,
- Chaulio R. Ferreira,
- Wenli Li
This paper proposes an efficient parallel heuristic for siting observers on raster terrains. More specifically, the goal is to choose the smallest set of points on a terrain such that observers located in these points are able to visualize at least a ...
Index Terms
- Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| BigSpatial '22 | 14 | 5 | 36% |
| BigSpatial '20 | 9 | 7 | 78% |
| BigSpatial '19 | 8 | 4 | 50% |
| BigSpatial '16 | 14 | 8 | 57% |
| BigSpatial '14 | 13 | 8 | 62% |
| Overall | 58 | 32 | 55% |