Installation might be a bit tricky - the underlying library is written
in C++. If problems occur (Tree::M is configured for linux, i.e. an
ELF-system using g++ and gcc) you need to change CXX in the Makefile.PL
and/or hack the GiST/Makefile and MT/Makefile themselves.
=============================================================================
NAME
Tree::M - implement M-trees for efficient "metric/multimedia-searches"
SYNOPSIS
use Tree::M;
$M = new Tree::M
DESCRIPTION
(not yet)
Ever had the problem of managing multi-dimensional (spatial) data but
your database only had one-dimensional indices (b-tree etc.)? Queries
like
select data from table where latitude > 40 and latitude < 50
and longitude> 50 and longitude< 60;
are quite inefficient, unless longitude and latitude are part of the
same spatial index (e.g. an R-tree).
An M-tree is an index tree that does not directly look at the stored
keys but rather requires a *distance* (a metric, e.g. a vector norm)
function to be defined that sorts keys according to their distance. In
the example above the distance function could be the maximum norm
("max(x1-x2, y1-y2)"). The lookup above would then be something like
this:
my $res = $M->range([45,55], 5);
This module implements an M-tree. Although the data structure and the
distance function is arbitrary, the current version only implements
n-dimensional discrete vectors and hardwires the distance function to
the suared euclidean metric (i.e. "(x1-x2)**2 + (y1-y2)**2 + (z1-z2)**2
+ ..."). Evolution towards more freedom is expected ;)
THE Tree::M CLASS
$M = new Tree::M ndims, min, max, steps
Creates a new M-Tree. Before it can be used you have to call one of
the "create" or "open" methods.
ndims the number of dimensions each vector has
min the lowest allowable scalar value in each dimension
max the maximum allowable number
steps the number of discrete steps (used when stored externally)
Example: create an M-Tree that stores 8-bit rgb-values:
$M = new Tree::M 3, 0, 255, 256;
Example: create an M-Tree that stores coordinates from -1..1 with
100 different steps:
$M = new Tree::M 2, -1, 1, 100;
$M->open(path)
$M->create($path)
Open or create the external storage file "$path" and associate it
with the tree.
[this braindamaged API will go away ;)]
$M->insert(\@v, $data)
Insert a vector (given by an array reference) into the index and
associate it with the value "$data" (a 32-bit integer).
$res = $M->range(\@v, $radius)
Search all entries not farther away from "@v" then "$radius" and
return an arrayref containing the searchresults.
Each result is again anarrayref composed like this:
[\@v, $data]
e.g. the same as given to the "insert" method.
$res = $M->top(\@v, $n)
Return the "$n" "nearest neighbours". The results arrayref (see
"range") contains the "$n" index values nearest to "@v", sorted for
distance.
$distance = $M->distance(\@v1, \@v2)
Calculcate the distance between two vectors, just as they databse
engine would do it.
$depth = $M->maxlevel
Return the maximum height of the tree (usually a small integer
specifying the length of the path from the root to the farthest
leaf)
BUGS
Inserting too many duplicate keys into the tree cause the C++ library to
die, so don't do that.
AUTHOR
Marc Lehmann .
SEE ALSO
perl(1), the DBIx::SpatialKeys manpage.