The goal of this library is to make ODBC recordsets look just like an STL container. As a user, you can move through our containers using standard STL iterators and if you insert(), erase() or replace() records in our containers changes can be automatically committed to the database for you. The library s compliance with the STL iterator and container standards means you can plug our abstractions into a wide variety of STL algorithms for data storage, searching and manipulation. In addition, the C++ reflection mechanism used by our library to bind to database tables allows us to add generic indexing and lookup properties to our containers with no special code required from the end-user. Because our code takes full advantage of the template mechanism, it adds minimal overhead compared with using raw ODBC calls to access a database.
penMesh is a generic and efficient data structure for representing and manipulating polygonal meshes. OpenMesh is developed at the Computer Graphics Group, RWTH Aachen , as part of the OpenSGPlus project, is funded by the German Ministry for Research and Education ( BMBF), and will serve as geometry kernel upon which the so-called high level primitives (e.g. subdivision surfaces or progressive meshes) of OpenSGPlus are built.
It was designed with the following goals in mind :
Flexibility : provide a basis for many different algorithms without the need for adaptation.
Efficiency : maximize time efficiency while keeping memory usage as low as possible.
Ease of use : wrap complex internal structure in an easy-to-use interface.
The Engineering Vibration Toolbox is a set of educational programs
written in Octave by Joseph C. Slater. Also included are a number of help files,
demonstration examples, and data files containing raw experimental data. The
codes include single degree of freedom response, response spectrum, finite
elements, numerical integration, and phase plane analysis.
This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
dimensionality and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster histogram, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
