Big data is an important area of research for data researchers and scientists. This area has seen significant interest from industry and federal agencies alike in the past decade. Within the realm of big data, spatial and spatio-temporal data are still one of the 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 continued to explode in recent years. In addition, significant progress in ground, air, and space-borne sensor technologies has led to unprecedented access to earth science data for scientists from different disciplines interested in studying the complementary nature of different parameters. Analysis of this data poses new challenges to researchers.
Proceeding Downloads
GLIN: A (G)eneric (L)earned (In)dexing Mechanism for Complex Geometries
Although spatial indexes shorten the query response time, they rely on complex tree structures to narrow down the search space. Such structures in turn yield additional storage overhead and take a toll on index maintenance. Recently, there have been a ...
Q-learning Based Simulation Tool for Studying Effectiveness of Dynamic Application of Fertilizer on Crop Productivity
As per the Food and Agriculture Organization (FAO), agricultural productivity needs to be increased by 70% to feed a projected 10 billion people by the year 2050, and fertilizer application plays a key role in achieving this goal. Throughout the world, ...
Processing of Spatial-Keyword Range Queries in Apache Spark
Big spatio-textual data are prevalent in modern applications, where spatial objects are associated with textual descriptions. For querying spatio-textual data, spatial-keyword queries have been proposed, which entail challenges mainly because of the ...
A Highly Efficient and Effective Attribute Learning Framework for Road Graph from Aerial Imagery and GPS
Road attributes play a pivotal role in digital maps, providing critical information for various routing and planning applications that aim to create a safe and efficient traffic environment. While some road attributes are available in existing map data ...
Index Terms
- Proceedings of the 11th 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% |