We present the development and use of a novel distributed geohazard modeling environment for the analysis and interpretation of large scale earthquake data sets. Our work demonstr...
Simulations of complex scientific phenomena involve the execution of massively parallel computer programs. These simulation programs generate large-scale multidimensional data set...
Multivariate time series (MTS) data sets are common in various multimedia, medical and financial application domains. These applications perform several data-analysis operations ...
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment histor...
Srivatsava Ranjit Ganta, Jyotsna Kasturi, John Gil...
—All sciences, including astronomy, are now entering the era of information abundance. The exponentially increasing volume and complexity of modern data sets promises to transfor...
Science is increasingly driven by data collected automatically from arrays of inexpensive sensors. The collected data volumes require a different approach from the scientists'...
Stuart Ozer, Jim Gray, Alexander S. Szalay, Andrea...
The detection of correlations between different features in high dimensional data sets is a very important data mining task. These correlations can be arbitrarily complex: One or...
The information revolution is creating and publishing vast data sets, such as records of business transactions, environmental statistics and census demographics. In many applicati...
- The ICP (Iterative Closest Point) algorithm remains a very popular method for the registration of 3D data sets, when an initial guess of the relative pose between them is known. ...