In this tutorial, we present the recent work in the database community for handling Big Spatial Data. This topic became very hot due to the recent explosion in the amount of spatial data generated by smart phones, satellites and medical devices, among others. This tutorial goes beyond the use of existing systems as-is (e.g., Hadoop, Spark or Impala), and digs deep into the core components of big systems (e.g., indexing and query processing) to describe how they are designed to handle big spatial data. During this 90-minute tutorial, we review the state-of-the-art work in the area of Big Spatial Data while classifying the existing research efforts according to the implementation approach, underlying architecture, and system components. In addition, we provide case studies of full-fledged systems and applications that handle Big Spatial Data which allows the audience to better comprehend the whole tutorial.
|Original language||English (US)|
|Number of pages||4|
|Journal||Proceedings of the VLDB Endowment|
|State||Published - Aug 1 2017|
|Event||43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany|
Duration: Aug 28 2017 → Sep 1 2017
Bibliographical noteFunding Information:
This work is supported in part by the National Science Foundation under Grants IIS-1525953, CNS-1512877, IIS-0952977, and IIS-1218168.