Big data is currently the hottest topic for data researchers and scientists with huge interests from the industry and federal agencies alike, as evident in the recent White House initiative on "Big data research and development". Within the realms of big data, spatial and spatio-temporal data is one of fastest growing types of data and poses a massive challenge to researchers who deal with analyzing such 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.
The 5th workshop on Analytics for Big Geospatial Data aims to bring together researchers from academia, government and industrial research labs who are working in the area of spatial analytics with an eye towards massive data sizes. The objective of this workshop is to provide a platform for researchers engaged in addressing the big data aspect of spatial and spatio-temporal data analytics to present and discuss their ideas. We invite participants from industry, academia, and government to participate in this event and share, contribute, and discuss the emerging big data challenges in the context of spatial and spatio-temporal data analysis.
The main motivation for this workshop stems from the increasing need for a forum to exchange ideas and recent research results, and to facilitate collaboration and dialog between academia, government, and industrial stakeholders.
Scalable analysis algorithms for spatial and spatio-temporal data mining
Novel applications on high performance computing frameworks (Clusters, GPU, cloud, Grid) for large scale spatial and spatio-temporal analysis
Performance studies comparing clouds, grids, and clusters for spatial and spatio-temporal analytics
Novel indexing methods for massive geospatial data
Visualization of massive geospatial data
Customizations and extensions of existing software infrastructures such as Hadoop for spatial, and spatiotemporal data mining
Applications of big data analysis: Climate Change, Disaster Management, Monitoring Critical Infrastructures, Transportation
10月31日
2016
会议日期
初稿截稿日期
注册截止日期
2014年11月04日 美国
第三届ACM 国际地理空间大数据分析研讨会2013年11月05日 美国
第二届ACM 国际大地理空间数据分析研讨会
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