—The importance of introducing distance constraints to data dependencies, such as differential dependencies (DDs) [28], has recently been recognized. The metric distance constrai...
The scatter plot is a well-known method of visualizing pairs of two-dimensional continuous variables. Multidimensional data can be depicted in a scatter plot matrix. They are intu...
Ming C. Hao, Umeshwar Dayal, Ratnesh K. Sharma, Da...
In P2P systems, large volumes of data are declustered naturally across a large number of peers. But it is very difficult to control the initial data distribution because every use...
The explosion of data in the biological community demands the development of more scalable and flexible portals for bioinformatic computation. To address this need, we put forth c...
Rory Carmichael, Patrick Braga-Henebry, Douglas Th...
In models that define probabilities via energies, maximum likelihood learning typically involves using Markov Chain Monte Carlo to sample from the model’s distribution. If the ...
Martinetz and Schulten proposed the use of a Competitive Hebbian Learning (CHL) rule to build Topology Representing Networks. From a set of units and a data distribution, a link i...
: Data distribution is one of the key aspects that a parallelizing compiler for a distributed memory architecture should consider, in order to get efficiency from the system. The ...
Distributed database design is a complex process that involves a set of distinct aspects for the accomplishment of an adequate data distribution (Buretta 1997). Many of these aspec...
Data mining applications place special requirements on clustering algorithms including: the ability to nd clusters embedded in subspaces of high dimensional data, scalability, end...
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopul...
A data distribution scheme of sparse arrays on a distributed memory multicomputer, in general, is composed of three phases, data partition, data distribution, and data compression...