Sentence examples for processing large amounts of data from inspiring English sources
Purpose: Has developed an open-source version of Google's MapReduce and File System infrastructure for processing large amounts of data in parallel, a necessity in cloud computing.
Yet processing large amounts of data uses lots of energy and can be slow because the computer chips have to continually fetch data from memory.
Invented in the 1880's by an engineer named Herman Hollerith to automate the United States Census, punch cards were the earliest widely used mechanical system for processing large amounts of data.
Implementation of INDE will be dependent on future advances in High Performance Computing systems approaching the exascale and parallel advances in the development of algorithms for processing large amounts of data.
As more data are provided in Semantic Web, processing large amounts of data with triple-format triple-data, and interlinking the applications with triples, have become important for a variety of applications.
Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, controlling co-placement and scheduling of data with compute resources, and storing, transferring, and managing large volumes of data.
Similarly, traditional data processing techniques have several limitations of processing large amount of data.
However, existing FHE schemes are inefficient for processing large amount of data in cloud and garbled circuits are one time programs and cannot be reused.
However, these technological advances have also brought new challenges to processing large amount of data in a bandwidth-limited, power-constraint, unstable and dynamic environment.
Therefore, identification of low-resistivity pay zones is a problem of mining and processing large amount of data with strong nonlinearity.
The fact that logic models are easy to compute makes them useful for random searches and screening (for example, analyzing perturbations at multiple elements of the network), and for processing large amounts of individualized data (for example, comparing proteomics data from tumor cells or mutants with data from their normal counterpart).
