Raptor: Large scale analysis of big raster and vector data

Samriddhi Singla, Ahmed Eldawy, Rami Alghamdi, Mohamed F. Mokbel

Research output: Contribution to journalConference article


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.

Original languageEnglish
Pages (from-to)1950-1953
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
Publication statusPublished - 1 Jan 2018
Event45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States
Duration: 26 Aug 201730 Aug 2017

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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