Please note: This master’s thesis presentation will be given online.
Kyle Langendoen, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Khuzaima Daudjee
Indexes for multidimensional data based on the R-Tree are popularly used by databases for a wide range of applications. Such index trees support point and range queries but are costly to construct over datasets of millions of points. We present the Non-Intersecting R-Tree (NIR-Tree), a novel insert-efficient, in-memory, multidimensional index that uses bounding polygons to provide twice as efficient point queries, and equivalent range query performance while indexing data up to an order of magnitude faster. The NIR-Tree leverages non-intersecting bounding polygons to reduce the number of nodes accessed during queries, compared to existing R-family indexes. Our experiments demonstrate that inserting into a NIR-Tree is 27x faster than the ubiquitous R*-Tree, and 1.2x faster than the Revised R*-Tree. Furthermore, point queries in the NIR-Tree complete 2.2x faster than in the R*-Tree, and 3.1x faster than in the Revised R*-Tree, while range queries execute just as quickly.
To join this master’s thesis presentation on Zoom, please go to https://us02web.zoom.us/j/5198884567.
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