Changing from computing grid to knowledge grid in life‐science grid

Abstract

Grid computing has a great potential to become a standard cyber infrastructure for life sciences that often require high-performance computing and large data handling, which exceeds the computing capacity of a single institution. Grid computer applies the resources of many computers in a network to a single problem at the same time. It is useful to scientific problems that require a great number of computer processing cycles or access to a large amount of data.As biologists,we are constantly discovering millions of genes and genome features, which are assembled in a library and distributed on computers around the world.This means that new, innovative methods must be developed that exploit the re-sources available for extensive calculations - for example grid computing.This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing a "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. By extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community.

Computational Epigenetics: the new scientific paradigm

“…The next few years will see increased interest in the use of cluster computing, central (cloud) computing and distributed systems for large-scale epigenetic data analysis and screening. Computing grid technologies harnessing the resources of multiple computers in a network have been developed rapidly to solve high-throughput scientific research problem [124]. On the other hand, cloud computing technologies, which offers scalable resources on demand, have emerged in recent years to complement the rate of data output and drive the rate of data analysis and knowledge discovery [125].…”

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“…The next few years will see increased interest in the use of cluster computing, central (cloud) computing and distributed systems for large-scale epigenetic data analysis and screening. Computing grid technologies harnessing the resources of multiple computers in a network have been developed rapidly to solve high-throughput scientific research problem [124]. On the other hand, cloud computing technologies, which offers scalable resources on demand, have emerged in recent years to complement the rate of data output and drive the rate of data analysis and knowledge discovery [125].…”

Microbial bioinformatics for food safety and production

“…Big data/grid/cloud/ With the increasing volume and heterogeneity of data sets (often referred to as “Big Data”), high performance computing is needed for analysis of the data. Many bioinformatics methods have been adapted to run on clusters of multiple computers (grid computing) and on large remotely located servers (cloud computing) [ 5 ]. Galaxy [ 6 ] and KNIME [ 7 ] are two popular software solutions to integrate and distribute larger data analysis tasks to the grid/cloud.…”