2016 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
Big earth observation data analytics: matching requirements to system architectures
- Gilberto Camara,
- Luiz Fernando Assis,
- Gilberto Ribeiro,
- Karine Reis Ferreira,
- Eduardo Llapa,
- Lubia Vinhas
Earth observation satellites produce petabytes of geospatial data. To manage large data sets, researchers need stable and efficient solutions that support their analytical tasks. Since the technology for big data handling is evolving rapidly, ...
Building knowledge graph from public data for predictive analysis: a case study on predicting technology future in space and time
A domain expert can process heterogeneous data to make meaningful interpretations or predictions from the data. For example, by looking at research papers and patent records, an expert can determine the maturity of an emerging technology and predict the ...
Spatial computing goes to education and beyond: can semantic trajectory characterize students?
Spatial big data (SBD) has been utilized in many fields and we propose SBD analytics to apply to education with semantic trajectory data of undergraduate students in Songdo International Campus at Yonsei University. Higher education is under a pressure ...
Towards massive spatial data validation with SpatialHadoop
Spatial data usually encapsulate semantic characterization of features which carry out important meaning and relations among objects, such as the containment between the extension of a region and of its constituent parts. The GeoUML methodology allows ...
Analytics on public transport delays with spatial big data
The increasing pervasiveness of location-aware technologies is leading to the rise of large, spatio-temporal datasets and to the opportunity of discovering usable knowledge about the behaviors of people and objects. Applied extensively in transportation,...
Big data as a service from an urban information system
Big Data has already proven itself as a valuable tool that lets geographers and urban researchers utilize large data resources to generate new insights. However, wider adoption of Big Data techniques in these areas is impeded by a number of difficulties ...
High-performance polyline intersection based spatial join on GPU-accelerated clusters
The rapid growing volumes of spatial data have brought significant challenges on developing high-performance spatial data processing techniques in parallel and distributed computing environments. Spatial joins are important data management techniques in ...
Agent based urban growth modeling framework on Apache Spark
The simulation of urban growth is an important part of urban planning and development. Due to large data and computational challenges, urban growth simulation models demand efficient data analytic frameworks for scaling them to large geographic regions. ...
Index Terms
- Proceedings of the 5th 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% |