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Distributed Data Mining and Big Data: Intel Vision Paper

This paper describes Intel’s perspective on distributed data mining with big data—or the analytics of big data generated by sensors and devices on the edge of networks. The paper includes a discussion of: • The importance of analytics at the edge of networks where some of “biggest,” fastest growing sources for big data are generated • How big data is inherently different from the data managed by traditional data management or business intelligence platforms, and why it matters • A quick overview of emerging technologies including distributed frameworks such as the Apache Hadoop* framework and Apache* MapReduce • Four edge analytics use cases for government (smart cities), retail (the connected store), automotive (intelligent systems on the road), and manufacturing (smart factories)—two utilizing the Hadoop* framework and two focused on intelligent systems data

This paper describes Intel’s perspective on distributed data mining with big data—or the analytics of big data generated by sensors and devices on the edge of networks. The paper includes a discussion of: • The importance of analytics at the edge of networks where some of “biggest,” fastest growing sources for big data are generated • How big data is inherently different from the data managed by traditional data management or business intelligence platforms, and why it matters • A quick overview of emerging technologies including distributed frameworks such as the Apache Hadoop* framework and Apache* MapReduce • Four edge analytics use cases for government (smart cities), retail (the connected store), automotive (intelligent systems on the road), and manufacturing (smart factories)—two utilizing the Hadoop* framework and two focused on intelligent systems data

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