Dear all, I'm a user of CafeTran (www.CafeTran.com) which uses H2 as a database engine (http://cafetran.wikidot.com/working-with-external-databases). I'm completely unexperienced with this feature of CafeTran. Before I start learning it, I'd like to ask a question here.
In another user group (http://groups.yahoo.com/neo/groups/help_/conversations/messages/40917) I found this quote: WFS integrates its own database engine. Most other solutions rely on > standard database systems, like Oracle™, Lucene, or SQL engines. A > standard database system (DBMS) may sound reassuring to executives who > don't grasp the nature of linguistic databases. But there are major > drawbacks. The first is that DBMSes are built for standard chores like > business management. They are not optimized for linguistic purposes. A > standard database system is fine for storing TMs, but its results are > poor when it comes to exploiting them full-speed in real production. A > TM engine, when looking for a match in a large TM, needs to access > then score thousands of candidate TUs before making its final choice – > all in well under a second. No wonder search engines (Google, Bing), > faced with a similar task of detecting fuzzy correspondances in large > masses of text, use their own proprietary database and indexing > scheme. My question would be: is H2 the best solution to store Translation Memories with millions of segments? Translation Memories are a special kind of XML files where every segment (e.g. a sentence) in a source language is assigned to a segment in a target language: http://en.wikipedia.org/wiki/Translation_Memory_eXchange Thanks for your help! Hans -- You received this message because you are subscribed to the Google Groups "H2 Database" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at http://groups.google.com/group/h2-database. For more options, visit https://groups.google.com/groups/opt_out.
