Full Citation

Title: Raptor: large scale analysis of big raster and vector data

Citation Type: Journal Article

Publication Year: 2019

ISBN:

ISSN: 21508097

DOI: 10.14778/3352063.3352107

NSFID:

PMCID:

PMID:

Abstract: With the increase in amount of remote sensing data, there have been efforts to efficiently process it to help ecologists and geographers answer queries. However, they often need to process this data in combination with vector data, for example, city boundaries. Existing efforts require one dataset to be converted to the other representation, which is extremely inefficient for large datasets. In this demonstration, we focus on the zonal statistics problem, which computes the statistics over a raster layer for each polygon in a vector layer. We demonstrate three approaches, vector-based, raster-based, and raptor-based approaches. The latter is a recent effort of combining raster and vector data without a need of any conversion. This demo will allow users to run their own queries in any of the three methods and observe the differences in their performance depending on different raster and vector dataset sizes.

Url: https://dl.acm.org/doi/10.14778/3352063.3352107

Url: http://dl.acm.org/citation.cfm?doid=3352063.3360423

User Submitted?: No

Authors: Singla, Samriddhi; Eldawy, Ahmed; Alghamdi, Rami; Mokbel, Mohamed F.

Periodical (Full): Proceedings of the VLDB Endowment

Issue: 12

Volume: 12

Pages: 1950-1953

Data Collections: IPUMS Terra

Topics: Population Data Science

Countries: