MonetDB[1] is good for applications where you load the data once and then run lots of joins over binary relations, assuming your application works well with an RM/T[2] style schema.
In RM/T you have one entity relation that holds your entity ids and one property relation for each attribute. All your queries become many-way joins as they must traverse all the property relations. This is something you would never do with an ordinary RDBMS but is exactly what MonetDB's binary-association table scheme is designed to handle[3].
With MonetDB, you use SQL to define your schema in the familiar way. At the physical level MonetDB stores your data in an RM/T-style representation.
You may also want to see if Proximity[4][5]'s QGraph is suitable for your application. It uses MonetDB in it's implementation. The tutorial[6] includes an example of how to use it with Jython.
For spatial processing, you may find this MonetDB application a lot more interesting:
Sloan Digital Sky Survey / SkyServer provides public access to SDSS for astronomers, students, and wide public. A project to make a map of a large part of the Universe: 230 million object images, 1 million spectra, 4TB catalog data, 9TB images
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MonetDB could be useful here.
MonetDB[1] is good for applications where you load the data once and then run lots of joins over binary relations, assuming your application works well with an RM/T[2] style schema.
In RM/T you have one entity relation that holds your entity ids and one property relation for each attribute. All your queries become many-way joins as they must traverse all the property relations. This is something you would never do with an ordinary RDBMS but is exactly what MonetDB's binary-association table scheme is designed to handle[3].
With MonetDB, you use SQL to define your schema in the familiar way. At the physical level MonetDB stores your data in an RM/T-style representation.
You may also want to see if Proximity[4][5]'s QGraph is suitable for your application. It uses MonetDB in it's implementation. The tutorial[6] includes an example of how to use it with Jython.
[1] http://monetdb.cwi.nl
[2] http://en.wikipedia.org/wiki/Relational_Model/Tasmania
[3] http://monetdb.cwi.nl/projects/monetdb/Assets/monetdb_lectur...
[4] http://kdl.cs.umass.edu/proximity
[5] http://kdl.cs.umass.edu/proximity/about.html
[6] http://kdl.cs.umass.edu/software/documentation/tutorial/ch06...
For spatial processing, you may find this MonetDB application a lot more interesting:
Sloan Digital Sky Survey / SkyServer provides public access to SDSS for astronomers, students, and wide public. A project to make a map of a large part of the Universe: 230 million object images, 1 million spectra, 4TB catalog data, 9TB images
http://www.win.tue.nl/~tcalders/presentations/ivanova.ppt
Thank you very much. Can you give me your email? I'm @gmail with the same YC username I use.