Big Data: Related Technologies, Challenges and Future by Min Chen

By Min Chen

This Springer short presents a finished assessment of the heritage and up to date advancements of massive information. the price chain of massive info is split into 4 levels: information new release, information acquisition, facts garage and knowledge research. for every section, the e-book introduces the final history, discusses technical demanding situations and stories the most recent advances. applied sciences less than dialogue comprise cloud computing, net of items, information facilities, Hadoop and extra. The authors additionally discover a number of consultant purposes of huge facts resembling firm administration, on-line social networks, healthcare and clinical functions, collective intelligence and shrewdpermanent grids. This booklet concludes with a considerate dialogue of attainable study instructions and improvement tendencies within the box. gigantic facts: similar applied sciences, demanding situations and destiny clients is a concise but thorough exam of this interesting zone. it's designed for researchers and pros drawn to significant facts or similar study. Advanced-level scholars in machine technology and electric engineering also will locate this ebook beneficial.

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Selected Topics in Quantum Electronics, IEEE Journal of, 17(2):384–395, 2011. 37. Xia Zhou, Zengbin Zhang, Yibo Zhu, Yubo Li, Saipriya Kumar, Amin Vahdat, Ben Y Zhao, and Haitao Zheng. Mirror mirror on the ceiling: Flexible wireless links for data centers. ACM SIGCOMM Computer Communication Review, 42(4):443–454, 2012. 38. Maurizio Lenzerini. Data integration: A theoretical perspective. In Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 233–246.

Strong Flexibility: Hadoop may handle many kinds of data from various sources. In addition, data from many sources can be synthesized in Hadoop for further analysis. Therefore, it can cope with many kinds of challenges brought by big data. • High Fault-Tolerance: it is common that data loss and miscalculation occur during the analysis of big data, but Hadoop can recover data and correct computing errors caused by node failures or network congestion. , spam filtering, network searching, clickstream analysis, and social recommendation.

In Proceedings of the 5th international conference on Embedded networked sensor systems, pages 103–116. ACM, 2007. 12. Guillermo Barrenetxea, François Ingelrest, Gunnar Schaefer, Martin Vetterli, Olivier Couach, and Marc Parlange. Sensorscope: Out-of-the-box environmental monitoring. In Information Processing in Sensor Networks, 2008. IPSN’08. International Conference on, pages 332–343. IEEE, 2008. 13. Younghun Kim, Thomas Schmid, Zainul M Charbiwala, Jonathan Friedman, and Mani B Srivastava. Nawms: nonintrusive autonomous water monitoring system.

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