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Meyer and Schroeder map the rise of digital research and offer case studies from many fields, including biomedicine, social science uses of the Web, astronomy, and large-scale textual analysis in the humanities.
In the paper we present the potential of the combination between new algorithms applied to large scale textual databases and the use of domain knowledge derived from engineering and physics.
We used raw sequence reads to examine large-scale sequence content and the repetitive structure of the lamprey genome.
This work is concerned with scaling decentralized content-based publish/subscribe (CBPS) networks for large-scale content distribution.
A decentralized content-based publish/subscribe (CBPS) network for large-scale content distribution is proposed in [26].
Two trends have emerged in the setting of large-scale content delivery networks like YouTube.
First, it has enabled nearly cost-free reproduction and large-scale distribution of digital content.
Moreover, large-scale applications such as content delivery and interactive worlds pose serious scalability problems for the Internet.
These can be used as a measuring stick to gauge the large-scale geometry and overall contents of the universe.
Current large-scale information sources (structural, textual and images) are designed to support general queries and lack the ability to support scenario-specific information navigation, gathering, and presentation.
Faced with an ever increasing traffic demand, content providers and content delivery networks set large-scale caching infrastructures to distribute copies of the web content across multiple locations.
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