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SIGSPATIAL PhD '16: Proceedings of the 3rd ACM SIGSPATIAL PhD Symposium
ACM2016 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Burlingame California 31 October 2016
ISBN:
978-1-4503-4584-2
Published:
31 October 2016
Sponsors:
ESRI, amazon, Google Inc., Microsoft, ORACLE, Facebook
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Abstract

It is our great pleasure to welcome you to the 3rd ACM SIGSPATIAL PhD Symposium; the Symposium is held in conjunction with the ACM SIGSAPTIAL conference. The PhD Symposium is a forum where PhD students gather with top professors, researchers, and practitioners in the SIGSPATIAL community. It provides an opportunity to both junior and senior PhD students to present their work and receive useful feedback and guidance to help them move forward toward completion of their PhD.

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short-paper
Public Access
Task assignment in spatial crowdsourcing: challenges and approaches
Article No.: 1, Pages 1–4https://doi.org/10.1145/3003819.3003820

Spatial crowdsourcing (a.k.a mobile crowdsourcing) is a new paradigm of data collection, which has been emerged in the last few years to enable workers to perform tasks in the physical world. The objective of spatial crowdsourcing is to outsource a set ...

short-paper
A generic dual grid pruning approach for significant hotspot detection
Article No.: 2, Pages 1–4https://doi.org/10.1145/3003819.3003821

Given a set of points in two dimensional space, statistically significant hotspot detection aims to detect locations where the concentration of points inside the hotspot is much higher than outside. Statistically significant hotspot detection is an ...

short-paper
AstroSpark: towards a distributed data server for big data in astronomy
Article No.: 3, Pages 1–4https://doi.org/10.1145/3003819.3003823

Large amounts of astronomical data are continuously collected. As a result, support of scalable and high performance query processing of such data has become increasingly necessary. Apache Spark has been widely adopted as a successor to Apache Hadoop ...

short-paper
Discovery of driving patterns by trajectory segmentation
Article No.: 4, Pages 1–4https://doi.org/10.1145/3003819.3003824

Telematics data is becoming increasingly available due to the ubiquity of devices that collect data during drives, for different purposes, such as usage based insurance (UBI), fleet management, navigation of connected vehicles, etc. Consequently, a ...

Contributors
  • University of California, Riverside

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