Authors:
Wendy Osborn
and
Marc Moreau
Affiliation:
University of Lethbridge, Canada
Keyword(s):
Spatial Access Methods, Very Large Object Sets, Performance.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Databases and Data Security
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Large Scale Databases
;
Non-Relational Databases
;
Performance Evaluation and Benchmarking
;
Query Languages and Query Processing
Abstract:
This paper presents an evaluation of the mqr-tree for indexing a database containing a very large number
of objects. Many spatial access methods have been proposed for handling either point and/or region data,
with the vast majority able to handle a limited number of instances of these data types efficiently. However,
many established and emerging application areas, such as recommender systems, require the management
and indexing of very large object sets, such as a million places of interest that are each represented with a
point. Using between one and five million points and objects, a comparison of both index construction and
spatial query evaluation is performed versus a benchmark spatial indexing strategy. We show that the mqr-tree
achieves significantly lower overlap and overcoverage when used to index a very large collection of objects.
Also, the mqr-tree achieves significantly improved query processing performance in many cases. Therefore,
the mqr-tree is a significant candid
ate for handling very large object sets for emerging applications.
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