Examples of using Large data sets in English and their translations into Russian
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Analysis and processing of large data sets;
Moreover, concurrent release of large data sets may, however, be made possible only by distribution through the use of high capacity electronic media.
Random sampling: random sampling supports large data sets.
The interactive access to these sometimes rather large data sets has complemented the more traditional electronic publishing channels/media.
The system quickly handles multiple requests with large data sets.
Dynamic downscaling Statistical downscaling requires access to large data sets and considerable expertise to derive the statistical relationships, and is therefore difficult to apply.
This allows you to create sophisticated Web-based applications and work with large data sets.
These applications are particularly effective at dealing with very large data sets that a person simply couldn't physically process.
Its primary function is to detect potential acts of terror through analysis of large data sets.
Processing(digitization and consolidation) large data sets, make-up of complex and large documents(product catalogs), and preparing them for publication on the internet/intranet;
In certain circumstances, the service requires large data sets as inputs.
He stated that there was still a lot to be learned from the large data sets being generated by new technologies, which could be better exploited by researchers to promote innovation in the region.
On application of spatial decomposition method for large data sets indexing.
Multiple imputation has been difficult for very large data sets due to the storage space and computing power required, though this limitation is reducing as technology advances.
Machine learning methods use mathematical algorithms to search for certain patterns in large data sets.
Transforming extremely large data sets into meaningful information reveals trends and patterns that will be useful to policymakers in the fields of health, education, agriculture and financial services.
This implies that the NSIs can quite easily make their large data sets available to the researchers.
However, without looking at residuals, the linear regression model(34) will be much easier to implementthan the nonlinear model(32), especially for large data sets.
The long time series of large data sets gathered by the International Cooperative Programmes(ICPs) illustrate either time or spatial variations of air pollution effects on the environment, materials and human health.
Supercomputers, high performance clusters, including accelerators computing, development of 2D and 3D visualization, servers,storage and processing of large data sets, etc.
Additionally, for large data sets, the near-random memory access patterns of many integer sorting algorithms can handicap them compared to comparison sorting algorithms that have been designed with the memory hierarchy in mind.
The advances in automation and communications open up opportunities for more efficient and time-saving observing schemes,which means that very large data sets can be obtained and handled.
Using the large data sets that it has been compiling since 2000, CDP helps investors and investment fund managers to move their capital away from sources of carbon emissions and advises national and local governments on policy formulation in that regard.
In order to address this problem, we provide a"pass by reference" mechanism(see Figure 7)that avoids the need to use the communication platform messaging layer to transport these large data sets.
Rapid development of information technologies and social networking led to massive digitalisation of information andtoday offers very large data sets, which are difficult to process with traditional methodologies and means.
At the same time, new technologies are making possible innovative methods of collecting, storing and analysing data, requiring additional efforts towards creating international standards andmethods for managing and disseminating large data sets.
There have been advances in bioinformatics and computational biology, including: in developing tools for, andidentifying existing shortcomings in, analyzing very large data sets; new algorithms for searching for gene sequences in genome databases; and the increasing potential to generate false positives with increasing data generation.
It raises strategic issues such as how to respond to the competition from private sector and research organisations, and how to deal with methodological issues related to confidentiality, processing,linking and managing large data sets.
Develop new methodologies to reflect the changes in data acquisition and the dramatic increase of the volume of data available, for example on topics such as noise anderror reduction in large data sets, pattern recognition and other methodological tools appropriate for"Big Data. .
At the same time, new technologies and the advent of geographic information systems are making possible innovative methods of collecting, storing and analysing data, requiring additional efforts towards creating international standards andmethods for managing and disseminating large data sets.