MapReduce

High-Definition Semantic Maps (Part I)

This is my first stab at a realistic data set (see the attachments for the original resolution):

http://kill.devc.at/system/files/mr-wo-docs-small.jpg

It shows the landscape around the theme MapReduce, a cloud computing technology about which semantic web people may or may not have heard.

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A Map of a Topic Map of MapReduce

Here is a visualisation of such a topic map:

http://kill.devc.at/system/files/mr3.nomap-small.jpg

Ok, ok, one map thing after the other:

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Semantic Time Series (Temporal)

Last two months I was busy completing some exploratory work on semantic time series. As I mentioned earlier, this is all part of capturing sensor observation values and derivative computations in a semantic network.

Temporal Processing

The temporal aspect of handling time series is handled now by a special processor, which - hey you should know me!

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Announce: Perl Parallel::MapReduce on CPAN

Following the release-early-release-often tradition I have uploaded Parallel::MapReduce onto CPAN. It follows the ideas about embedding MapReduce more into the language, and to offer a pipelining feature.

But needless to say, this is all very pre-alpha, so tread carefully at your own risk and let me know what you think about the roadmap.

Look Ma! No files!

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MapReduce Pipeline in Perl

Perl's attitude to things is to be as unobtrusive as possible. Well, at least this is my approach.

In terms of using MapReduce inside a Perl application the following API should suffice:

use Parallel::MapReduce;
my $mri = new Parallel::MapReduce (...);

my $B = {1 => 'this is something ',
         2 => 'this is something else',
         3 => 'something else completely'};
my $C = $mri->mapreduce (
                         sub { ... mapper here ... },
                         sub { ... reducer here .. },
                         $B);

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MapReduce in a Bottle

You probably have heard about MapReduce, the method to highly parallelize certain algorithms so that they can be run on large processing farms. Several more-or-less good introductions of how this works exist, but most of them suffer from lots of technological noise, distracting from the core concepts.

Here - mostly for my own understanding - I try to recapture what MapReduce does, and I try to be as minimalistic as possible, even throwing away first MapReduce's biggest feature: parallelism.

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Topic Maps are Maps!

A while back I ranted that topic maps are normally not visualized as (quasi) geographical maps. I argued that the map metapher is so natural to most of us that lifting it into a semantic space is worth a try.

http://kill.devc.at/system/files/test.jpg

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