Efficient massive spatial data search with version control

In large scale earth observation systems, data is gathered in short duration surveys returning massive numbers of point data observations (often millions) covering significantly sized regions. To maintain
up-to-date products (e.g. navigation charts in harbours with continuous silt build-up), successive surveys are performed that often cover overlapping areas. Searches to find all the data in a specific region are expected to return only the most recent data in any given area, and must ignore any older, overlapping versions. In some cases, the data has errors that are difficult to correct; we would like to ignore or exclude this data during retrieval so that products derived from the data (e.g. contours on navigational charts) are accurate. This MITACS project investigates how to build a spatial data structure that efficiently supports this use case, termed “exclusion persistence range search”, over multiple versions of data covering the same area.

Faculty Supervisor:

Bradford Nickerson

Student:

Partner:

CARIS

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of New Brunswick

Program: