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Big data challenges in information engineering curriculum

机译:信息工程课程中的大数据挑战

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The amount of accumulated data is growing at unprecedented rates. This data is mainly unstructured or semi structured and comes from different sources in a variety of forms. Recently, a range of supporting storage and distributed parallel computing technologies have been developed and put into use by the sector.The products and implementations include; Apache's Hadoop, data processing framework Map Reduce, distributed big data management system Cassandra and other NoSQL database and data storage systems. Among the major challenges are; data representation, reliable shared storage, efficient algorithms and scalable distributed HW/SW infrastructures. Surprisingly, current curricula lack the necessary components to create awareness and a good understanding of these state of the art concepts and technologies. There is an urgent need for integrating the developments in big data technologies into the educational programs and computing curricula. This need not only is dictated by the industry, but also by the employement dynamics in the related professions. This paper discusses fundemental big data issues and technologies that are considered to be necessary for the existing educational programs in computing, information systems and information engineering areas.
机译:累积的数据量正以前所未有的速度增长。这些数据主要是非结构化或半结构化的,并且来自各种形式的不同来源。最近,该行业已经开发并使用了一系列支持存储和分布式并行计算技术。产品和实现包括: Apache的Hadoop,数据处理框架Map Reduce,分布式大数据管理系统Cassandra和其他NoSQL数据库和数据存储系统。主要挑战包括:数据表示,可靠的共享存储,高效的算法和可扩展的分布式硬件/软件基础架构。令人惊讶的是,当前的课程缺乏必要的组成部分来提高对这些最新概念和技术的认识和良好理解。迫切需要将大数据技术的发展整合到教育计划和计算机课程中。这种需求不仅是由行业决定的,而且还取决于相关行业的就业动态。本文讨论了基本的大数据问题和技术,这些问题和技术被认为是计算,信息系统和信息工程领域中现有教育计划所必需的。

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