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Scholarly documents contain many data entities, such as titles, authors, affiliations, figures, and tables. These entities can be used to enhance digital library services through enhanced metadata and enable the development of new services and tools for interacting with and exploring scholarly data. However, in a world of scholarly big data, extracting these entities in a scalable, efficient and accurate...
We introduce a big data platform that provides various services for harvesting scholarly information and enabling efficient scholarly applications. The core architecture of the platform is built on a secured private cloud, crawls data using a scholarly focused crawler that leverages a dynamic scheduler, processes by utilizing a map reduce based crawl-extraction-ingestion (CEI) workflow, and is stored...
With the rapid development of big data and cloud computing, big data analytics as a service in the cloud is becoming increasingly popular. More and more individuals and organizations tend to rent virtual cluster to store and analyze data rather than building their own data centers. However, in virtualization environment, whether scaling out using a cluster with more nodes to process big data is better...
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