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Towards building a scholarly big data platform: Challenges, lessons and opportunities

机译:建立学术型大数据平台:挑战,经验教训和机遇

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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 in distributed repositories and databases. Services such as scholarly data harvesting, information extraction, and user information and log data analytics are integrated into the platform and provided by an OAI and RESTful API. We also introduce a set of scholarly applications built on top of this platform including citation recommendation and collaborator discovery.
机译:我们引入了一个大数据平台,该平台提供各种服务来收集学术信息并实现有效的学术应用。该平台的核心架构建立在安全的私有云上​​,使用以学术为重点的爬虫来抓取数据,该爬虫利用动态调度程序,通过利用基于地图约简的抓取提取吸入(CEI)工作流程进行处理,并存储在分布式存储库中和数据库。诸如学术数据收集,信息提取以及用户信息和日志数据分析之类的服务已集成到平台中,并由OAI和RESTful API提供。我们还介绍了基于此平台构建的一组学术应用程序,包括引文推荐和合作者发现。

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