The paper deals with the optimization of processing of large volume of geographic data. The essence of the method is hierarchical decomposition of the set of processes into elementary processes and the allocation of means to these processes. The means can be of three types: hardware, software or human factor, eventually combination of these types. Each elementary process can be processed at one of these means in certain time. Generally, the processes and the means can be interdependent or independent. The described problem can be represented using an oriented graph, where nodes correspond to the processes or the means and edges represent either the interdependence of processes and means, or the processing time of certain process on a given mean. The map of processes is formed on the basis of the graph. This map contains temporal continuity of solutions of sub-processes. Then, the duration of all processes is compiled from this map, which must be less than the time solving a task in the required quality of results. If not, the pairs of sub process –mean are replaced alternative pairs according to the map of processes with lower duration. The special algorithm was designed for this task. If the sum of the durations of all processes complies with solutions, the optimization ends and at this

Scientists and intelligence analysts are interested in quickly discovering new results from the vast amount of available geospatial data. The key issues that arise in this pursuit are how to cope with new and changing information and how to manage the steadily increasing amount of available data

from the fact that large simulations in engineering, computer science, economics, and military apphcations, to mention a few, consume enormous amounts of time

Elliptic curves have been intensively studied in number theory and algebraic geometry for over 100 years and there is an enormous amount of literature on the subject. To quote the mathematician Serge Lang: It is possible to write endlessly on elliptic curves. (This is not a threat.) Elliptic curves

Fab is a recommendation system designed to help users sift through the enormous amount of information available in the World Wide Web. Operational since Dec. 1994, this system combines the content-based and collaborative methods of recommendation in a way that exploits the advantages of the two

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hashtable based methods has been developed, including MAQ, which is accurate, feature rich and fast enough

Output per worker varies enormously across countries. Why? On an accounting basis, our analysis shows that differences in physical capital and educational attainment can only partially explain the variation in output per worker — we find a large amount of variation in the level of the Solow

We consider a general model of the non-cooperative provision of a public good. Under very weak assumptions there will always exist a unique Nash equilibrium in our model. A small redistribution of wealth among the contributing consumers will not change the equilibrium amount of the public good

Busy-wait techniques are heavily used for mutual exclusion and barrier synchronization in shared-memory parallel programs. Unfortunately, typical implementations of busy-waiting tend to produce large amounts of memory and interconnect contention, introducing performance bottlenecks that become

trade between industrialized countries by moderate amounts of 20–50 percent.