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Networking Big Data: Definition, Key Technologies and Challenging Issues of Transmission

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Big Data Computing and Communications (BigCom 2015)

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Abstract

The big data has been touted as the new oil, which is expected to transform our society. Specially, the data source from the networking domain (networking big data) has higher volume, velocity, and variety compared with others. Thus in this article, we make a short survey on existing works investigating key technologies of networking big data, and propose challenging issues of transmission that is the most important stage for networking big data.

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References

  1. http://en.wikipedia.org/wiki/Big_data

  2. James, M.: Big Data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute (2011)

    Google Scholar 

  3. Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. IEEE/ACM Trans. Commun 51(1), 107–113 (2008)

    Google Scholar 

  4. A comprehensive list of big data statistics. http://wikibon.org/blog/big-data-statistics/

  5. Manyika, J., et al.: Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, pp. 1–137 (2011)

    Google Scholar 

  6. Sagiroglu, S., Sinanc, D.: Big data: a review. In: Proc. CTS, pp. 42–47 (2013)

    Google Scholar 

  7. Song, Y., Alatorre, G., Mandagere, N., et al.: Storage mining: where IT management meets big data analytics. In: Proc. Big Data, pp. 421–422 (2013)

    Google Scholar 

  8. Wang, Y., Jin, X.: Network big data: present and future. Chinese Journal of Computers 36(6), 1–15 (2013)

    Google Scholar 

  9. Ghemawat, S., Gobioff, H., Leung, S.T.: The google system. In: Proc. SOSP, pp. 29–43 (2003)

    Google Scholar 

  10. McKusick, M.K., Quinlan, S.: GFS: Evolution on fast-forward. ACM Queue 7(7), 10–20 (2009)

    Article  Google Scholar 

  11. Hadoop: distributed file system (2013). http://hadoop.apache.org/docs/r1.0.4/hdfsdesign.html

  12. Kosmosfs. https://code.google.com/p/kosmosfs/

  13. Chaiken, R., et al.: Scope: Easy and efficient parallel processing of massive data sets. In: Proc. VLDB, pp. 1265–1276 (2008)

    Google Scholar 

  14. Beaver, D., Kumar, S., Li, H.C., Sobel, J., Vajgel, P.: Finding a needle in Haystack: facebook’s photo storage. In: Proc. SOSDI, pp. 1–8 (2010)

    Google Scholar 

  15. Taobao file system. http://code.taobao.org/p/tfs/src/

  16. Fast distributed file system. https://code.google.com/p/fastdfs/

  17. DeCandia, G.: Dynamo: Amazon’s highly available key-value store. SIGOPS Oper. Syst. Rev. 41(6), 205–220 (2007)

    Article  Google Scholar 

  18. Karger, D., Lehman, E., Leighton, T., Panigrahy, R., Levine, M., Lewin, D.: Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the world wide web. In: Proc. STC, pp. 654–663 (1997)

    Google Scholar 

  19. Voldemort. http://www.project-voldemort.com/voldemort/

  20. Redis. http://redis.io/

  21. Tokyo Canbinet. http://fallabs.com/tokyocabinet/

  22. Tokyo Tyrant. http://fallabs.com/tokyotyrant/

  23. Memcached. http://memcached.org/

  24. MemcacheDB. http://memcachedb.org/

  25. Riak. http://basho.com/riak/

  26. Scalaris. http://code.google.com/p/scalaris/

  27. Chang, F., et al.: Bigtable: A distributed storage system for structured data. IEEE/ACM Trans. Comput. Syst. 26(2), 1–26 (2008)

    Article  Google Scholar 

  28. Burrows, M.: The chubby lock service for loosely-coupled distributed systems. In: Proc. SOSDI, pp. 335–350 (2006)

    Google Scholar 

  29. Lakshman, A., Malik, P.: Cassandra: structured storage system on a p2p network. In: Proc. SPDC, pp. 1–5 (2009)

    Google Scholar 

  30. HBase. http://hbase.apache.org/

  31. Hypertable. http://hypertable.org/

  32. RFC 4627-The application/JSON media type for Javascript object notation (JSON). http://tools.ietf.org/html/rfc4627

  33. MongoDB. http://www.mongodb.org/

  34. Hu, H., Wen, Y., Chua, T., Li, X.: Towards scalable systems for big data analytics: a technology tutorial. IEEE ACCESS, 652–687 (2014)

    Google Scholar 

  35. Hinton, G.E.: Learning multiple layers of representation. Trends Cog-nit. Sci. 11(10), 428–434 (2007)

    Article  Google Scholar 

  36. Baah, G.K., Gray, A., Harrold, M.J.: On-line anomaly detection of deployed software: a statistical machine learning approach. In: Proc. SQA, pp. 70–77 (2006)

    Google Scholar 

  37. Moeng, M., Melhem, R.: Applying statistical machine learning to multicore voltage and frequency scaling. In: Proc. Comput. Frontiers, pp. 277–286 (2010)

    Google Scholar 

  38. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: A review. ACM SIGMOD Rec. 34(2), 18–26 (2005)

    Article  Google Scholar 

  39. Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining. ACM SIGMOD Rec. 33(1), 50–57 (2004)

    Article  Google Scholar 

  40. Vander, W.A.: Process mining: Overview and opportunities. IEEE/ACM Trans. Manag. Inform. Syst. 3(2), 1–17 (2012)

    Google Scholar 

  41. Ritter, A., Clark, S., Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proc. EMNLP, pp. 1524–1534 (2011)

    Google Scholar 

  42. Li, Y., Hu, X., Lin, H., Yang, Z.: A framework for semisupervised feature generation and its applications in biomedical literature mining. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(2), 294–307 (2011)

    Article  Google Scholar 

  43. Blei, D.M.: Probabilistic topic models. IEEE/ACM Trans. Commun. 55(4), 77–84 (2012)

    MathSciNet  Google Scholar 

  44. Balinsky, H., Balinsky, A., Simske, S.J.: Automatic text summarization and small-world networks. In: Proc. SDE, pp. 175–184 (2011)

    Google Scholar 

  45. Mishra, M., Huan, J., Bleik, S., Song, M.: Biomedical text categorization with concept graph representations using a controlled vocabulary. In: Proc. DMB, pp. 26–32 (2012)

    Google Scholar 

  46. Huet, J., et al.: Enhancing text clustering by leveraging wikipedia semantics. In: Proc. RDIR, pp. 179–186 (2008)

    Google Scholar 

  47. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inform. Retr. 2(1), 1–135 (2008)

    Article  Google Scholar 

  48. Pal, S.K., Talwar, V., Mitra, P.: Web mining in soft computing framework: Relevance, state of the art and future directions. IEEE Trans. Neural Netw. 13(5), 1163–1177 (2002)

    Article  Google Scholar 

  49. Chen, X., Lin, X.: Big data deep learning: challenges and perspectives. IEEE Access, 514–525 (2014)

    Google Scholar 

  50. Olshannikova, E., Ometov, A., Koucheryavy, Y.: Towards big data visualization for augmented reality. In: Proc. CBI, pp. 33–37 (2014)

    Google Scholar 

  51. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: Proc. SOSDI, pp. 137–150 (2004)

    Google Scholar 

  52. Lam, W., Liu, L., Prasad, S., Rajaraman, A., Vacheri, Z., Doan, A.: Muppet: mapreduce style processing of fast data. In: Proc. VLDB, pp. 1814–1825 (2012)

    Google Scholar 

  53. Condie, T., Conway, N., Alvaro, P., Hellerstein, J.M., Elmeleegy, K., Sears, R.: MapReduce online. In: Proc. NSDI (2010)

    Google Scholar 

  54. Logothetis, D., Yocum, K.: Ad-hoc data processing in the cloud. In: Proc. VLDB, pp. 1472–1475 (2008)

    Google Scholar 

  55. Brito, A., Martin, A., Knauth, T., Creutz, S., Becker, D., Weigert, S., Fetzer, C.: Scalable and low-latency data processing with stream MapReduce. In: Proc. CCTS, pp. 48–58 (2011)

    Google Scholar 

  56. Suto, K., Nishiyama, H., Kato, N.: Context-aware task allocation for fast parallel big data processing in optical-wireless networks. In: Proc. IWCMC, pp. 423–428 (2014)

    Google Scholar 

  57. Sun, W., Li, F., Guo, W., Jin, Y., Hu, W.: Store, schedule and switch–a new data delivery model in the big data era. In: Proc. ICTON, pp. 1–4 (2013)

    Google Scholar 

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Correspondence to Weigang Hou .

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Hou, W., Guo, P., Guo, L. (2015). Networking Big Data: Definition, Key Technologies and Challenging Issues of Transmission. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-22047-5_9

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