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External Memory Geometric Data Structures Department of Computer Science
 

Summary: External Memory Geometric Data Structures
Lars Arge
Department of Computer Science
University of Aarhus and Duke University
Augues 24, 2005
1 Introduction
Many modern applications store and process datasets much larger than the main memory of even
state-of-the-art high-end machines. Thus massive and dynamically changing datasets often need
to be stored in space efficient data structures on external storage devices such as disks. In such
cases the Input/Output (or I/O) communication between internal and external memory can be-
come a major performance bottleneck. Many massive dataset applications involve geometric data
(for example points, lines, and polygons) or data that can be interpreted geometrically. Such appli-
cations often perform queries that correspond to searching in massive multidimensional geometric
databases for objects that satisfy certain spatial constraints. Typical queries include reporting the
objects intersecting a query region, reporting the objects containing a query point, and reporting
objects near a query point.
While development of practically efficient (and ideally also multi-purpose) external memory
data structures (or indexes) has always been a main concern in the database community, most
data structure research in the algorithms community has focused on worst-case efficient internal
memory data structures. Recently however, there has been some cross-fertilization between the two

  

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

 

Collections: Computer Technologies and Information Sciences