New MaJorToM Topic Map Store based on redis
Published by {{by}} on {{at}} and updated at {{updated}}.
Abstract:
Today we added a new backend to the powerful Topic Maps engine MaJorToM: the Redis Topic Map Store. Redis is an open source, advanced key-value-store supporting strings, sets, hashes and lists as values. The code is published at the MaJorTom Google Code project.
Today we added a new backend to the powerful Topic Maps engine MaJorToM: the Redis Topic Map Store. Redis is an open source, advanced key-value-store supporting strings, sets, hashes and lists as values. The code is published at the MaJorTom Google Code project.
The new topic map store is the perfect combination of a light-weight and persistent data store. It enables a parallel access without the need to implement own thread locking mechanism. Redis is easy to administrate and enables very fast read and write access. Additionally it allows replication to multiple servers using its built-in sharding functionality.
To make a topic map storage within a Redis database, we recreated the schema of the Topic Maps Data Model based on the Redis possibilities. The Schema is documented . We are looking forward for comments on both the Redis store and the schema.
Update 2011-04-14: Johannes Schmidt has published another proposal for mapping of the Topic Maps Data Model to Redis schema.
Authors of this document are
Benjamin Bock
http://twitter.com/bnjmnbck
Benjamin is project leader of Ruby Topic Maps and rtm-tmql.
Subject Matter
MaJorToM
is a {{project}}.
MaJorToM is more than just a Topic Maps engine, it's a kind of philosophy of designing an independent Topic Maps engine. The MaJorToM API extends ...
Visit homepage of MaJorToM
glossary
Topic Maps Engine
is associated with {{count}} items.
A Topic Maps engine is a system based on the Topic Map ISO standard exposing a TMAPI compatible interface for running Topic Map applications.
Nowadays Topic Maps appears to be very suitable for being applied in Libraries, Archives and Museums. It provides a suitable model and mechanisms for depth indexing, classification and the implementation of FRBR, CIDOC-CRM or other conceptualizations.
