2018 Proceeding
The current trends in technology such as mobile phones, tablet devices, stationary sensors, satellites and IOT devices, motivate users voluntarily sharing their information, produce a huge flood of geo-spatial data enriched by multiple types of information or contexts, such as social, text, temporal data, multimedia data, and scientific measurements. This data flood provides a tremendous potential of discovering new and useful knowledge. The novel research challenge is to search and mine this wealth of multi-enriched geo-spatial data.
Proceeding Downloads
Spatiotemporal Traffic Volume Estimation Model Based on GPS Samples
Effective road traffic assessment and estimation is crucial not only for traffic management applications, but also for long-term transportation and, more generally, urban planning. Traditionally, this task has been achieved by using a network of ...
Density-based Multimodal Spatial Clustering using Pre-trained Deep Network for Extracting Local Topics
Users on social networking services (SNSs) have been transmitting information about events they witnessed themselves in their daily life through geo-social data as geo-tagged texts and photos. Geo-social data are usually related to not only personal ...
Data Fusion of Diverse Data Sources: Enrich Spatial Data Knowledge Using HINs
A range of GPS, social network and transportation applications have been developed, targetting to improve the quality of life of the user. Furthermore, the development of smart devices allows the user to use the applications any time, while also ...
Reach Me If You Can: Reachability Query in Uncertain Contact Networks
With the advent of reliable positioning technologies and prevalence of location-based services, it is now feasible to accurately study the propagation of items such as infectious viruses, sensitive information pieces, and malwares through a population ...
Applying Spatial Database Techniques to Other Domains: a Case Study on Top-k and Computational Geometric Operators
In this seminar, we will explore how processing rich spatial data is not the only practical (and research-wise promising) application domain for traditional spatial database techniques. An equally promising direction, possibly with low-hanging fruits ...
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| GeoRich '20 | 9 | 4 | 44% |
| GeoRich '17 | 10 | 8 | 80% |
| GeoRich '16 | 18 | 8 | 44% |
| GeoRich'15 | 13 | 5 | 38% |
| Overall | 50 | 25 | 50% |