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Gridded spatial datasets arise naturally in environmental, climatic, meteorological, and ecological settings. Each grid point encapsulates a vector of variables representing different measures of interest. Gridded datasets tend to be voluminous since they encapsulate observations for long timescales. Visualizing such datasets poses significant challenges stemming from the need to preserve interactivity, manage I/O overheads, and cope with data volumes. Here we present our methodology to significantly alleviate I/O requirements by leveraging deep neural network-based models and a distributed, in-memory cache to facilitate interactive visualizations. Our benchmarks demonstrate that deploying our lightweight models coupled with back-end caching and prefetching schemes can reduce the client's query response time by 92.3% while maintaining a high perceptual quality with a PSNR (peak signal-to-noise ratio) of 38.7 dB.
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Griddle: Effective Query Support over Voluminous Gridded Spatial Datasets
Gridded datasets occur in several domains. These datasets comprise (un)structured grid points, where each grid point is characterized by XY(Z) coordinates in a spatial referencing system. The data available at individual grid points are high-dimensional encapsulating multiple variables of interest. This study has two thrusts. The first targets supporting effective management of voluminous gridded datasets while reconciling challenges relating to colocation and dispersion. The second thrust is to support sliding (temporal) window queries over the gridded dataset. Such queries involve sliding a temporal window over the data to identify spatial locations and chronological time points where the specified predicate evaluates to true. Our methodology includes support for a space-efficient data structure for organizing information within the data, query decomposition based on dyadic intervals, support for temporal anchoring, query transformations, and effective evaluation of query predicates. Our empirical benchmarks are conducted on representative voluminous high dimensional datasets such as gridMET (historical meteorological data) and MACA (future climate datasets based on the RCP 8.5 greenhouse gas trajectory). In our benchmarks, our system can handle throughputs of over 3000 multi-predicate sliding window queries per second.
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- Award ID(s):
- 1931363
- PAR ID:
- 10448813
- Date Published:
- Journal Name:
- 2022 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 792 to 799
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData55660.2022.10020796
