"data frameworks" is correct and usable in written English.
It is a general term which can be used in any context to refer to a system which is used to store and organize data. For example, "The company implemented a new data framework to store customer information."
New databases and data frameworks, like Hadoop, have arisen to capture and exploit this.
An attractive approach for scaling the problem with data is to use Big data frameworks.
For this reason, the first step in choosing between big data frameworks is to determine if they are needed.
There are a growing number of different programming models that are used to describe algorithms within Big data frameworks.
We have also reviewed various big data frameworks with respect to underlying data sources, analytical capability and application areas.
We present a model library conceived to design and assess critical components of big data frameworks, with a control-centric approach.
First, the lack of a joined-up monitoring and data framework is not helping.
New York will still work with Wireless Generation indirectly, as part of a consortium of states building a shared data framework known as the Shared Learning Collaborative.
Jury Konga, who has been helping to create the G4 Open Data Framework (you can see some of his presentations here) says cities all over Canada are jumping onto the open data train: you can see our collection of Canadian open data sites here.
This exercise also provides a data framework with which to model regionally plausible Future Human Footprint scenarios.
An alternative approach to handle heterogeneity in a count data framework is to use latent class (or finite mixture) models.
