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From: Dirk Eddelbuettel
Subject: hdf5
Date: Wed, 16 Aug 2000 23:54:41 -0400 (EDT)

(header trimmed as hdf5 content is not relevant for message)

  "Daniel" == Daniel Heiserer <address@hidden> writes:
  Daniel> If hdf5 is not a database, is it hard to "combine" some databases
  Daniel> like BerkeleyDB or GDB with hdf5 to create a scientific database?

You don't say what you want to do with your data, whether it changes, whether
you need subqueries etc 

If you "merely" want to load and save lots of (static) matrices, I would
conjecture that the builtin formats load and save will be hard to beat in
terms of both speed and storage efficiency. You can easily build yourself
small C, C++, Perl, ... programs that read/write in the same format as it
"really" is just double array which you can store / retrieve with one

If however you want to query "relations" between your data, then a relational
database might be your choice.  I have used Octave with timeseries data on
Sybase (expensive, proprietary) as well as PostgreSQL (free, BSD-style
copyright).  I used Perl glue code (called from an .m file) back when I used
Sybase, and I wrote two C++ .oct functions I posted here a long time ago
which go directly to PostgreSQL.  Just as a reference, Bill Sharpe at
Stanford (who is also a winner of an Economics Nobel price) has a little
timeseries database system which is implemented direcly in Matlab and which
might be portable.

That being said, there are limits to what you can do due to design of the
Octave (Matlab) language.  For full-blown data analysis, the S language is
better, and I have very good recent experiences with the GNU R implementation
of it and both the R interface to PostgreSQL as well as the ODBC interface.

According to the latest figures, 43% of all statistics are totally worthless.

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