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I/O-efficient Point Location using Persistent B-Trees Lars Arge, Andrew Danner, and Sha-Mayn Teh

Summary: I/O-efficient Point Location using Persistent B-Trees
Lars Arge, Andrew Danner, and Sha-Mayn Teh
Department of Computer Science, Duke University
We present an external planar point location data structure that is I/O-efficient both in theory
and practice.
The developed structure uses linear space and answers a query in optimal O(logB N) I/Os,
where B is the disk block size. It is based on a persistent B-tree, and all previously developed
such structures assume a total order on the elements in the structure. As a theoretical result of
independent interest, we show how to remove this assumption.
Most previous theoretical I/O-efficient planar point location structures are relatively compli-
cated and have not been implemented. Based on a bucket approach, Vahrenhold and Hinrichs
therefore developed a simple and practical, but theoretically non-optimal, heuristic structure. We
present an extensive experimental evaluation that shows that, on a range of real-world Geographic
Information Systems (GIS) data, our structure uses fewer I/Os than the structure of Vahrenhold
and Hinrichs to answer a query. On a synthetically generated worst-case dataset, our structure
uses significantly fewer I/Os.
The planar point location problem is the problem of storing a planar subdivision
defined by N line segments such that the region containing a query point p can
be computed efficiently. Planar point location has many applications in, e.g., Ge-


Source: Arge, Lars - Department of Computer Science, Aarhus Universitet


Collections: Computer Technologies and Information Sciences