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How the Big Data Is Leading the Evolution of ICT Technologies and Processes

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Modeling and Processing for Next-Generation Big-Data Technologies

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 4))

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Abstract

This chapter has the main aim of providing an overview of the evolution process related to big data and its impact on the organization of ICT-related companies and enterprises. It starts from the severe scalability limits and performance issues introduced by the need of accessing massive amounts of distributed information, by highlighting the most important innovation trends, and developments characterizing this new architectural scenario both from the technological and the organizational perspectives. By trying to address the missing links in the ICT big picture, we also present the emerging data-driven reference models and solutions in order to give a clearer vision of the near future in the modern information-empowered society, where all the activities are more and more frequently conducted in very large collaborative partnerships involving multiple people and equipment scattered throughout the world.

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References

  1. International Telecommunication Union: World telecommunication/ict indicators database, 16th edn. (2012)

    Google Scholar 

  2. YouTube: Statistics (November 2013), http://www.youtube.com/yt/press/statistics.html

  3. Brumfiel, G.: Down the petabyte highway. Nature 469(20), 282–283 (2011)

    Google Scholar 

  4. Lefevre, C.: Lhc: the guide (January 2008), http://cds.cern.ch/record/1092437/files/CERN-Brochure-2008-001-Eng.pdf

  5. Open Government Initiative: Open government data, http://opengovernmentdata.org/

  6. McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity (2011), http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation

  7. McKinsey Global Institute: Disruptive technologies: Advances that will transform life, business, and the global economy (2013), http://www.mckinsey.com/insights/business_technology/disruptive_technologies

  8. Loecher, M., Jebara, T.: Citysense: Multiscale space time clustering of gps points and trajectories. In: Proceedings of the Joint Statistical Meeting (2009)

    Google Scholar 

  9. IDC iView: Big data, bigger digital shadows, and biggest growth in the far east (2012), http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf

  10. Meeker, M., Wu, L.: Kpcb internet trends (2013), http://www.kpcb.com/insights

  11. Bonwick, J., Ahrens, M., Henson, V., Maybee, M., Shellenbaum, M.: The zettabyte file system. In: Proc. of the 2nd Usenix Conference on File and Storage Technologies (2003)

    Google Scholar 

  12. Nelson, M.R.: Lzw data compression. Dr. Dobb’s Journal 14(10), 29–36 (1989)

    Google Scholar 

  13. Welch, T.A.: A technique for high-performance data compression. Computer 17(6), 8–19 (1984)

    Article  Google Scholar 

  14. Ziv, J., Lempel, A.: Compression of individual sequences via variable-rate coding. IEEE Transactions on Information Theory 24(5), 530–536 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  15. Gailly, J.L., Adler, M.: The gzip compressor (1999), http://www.gzip.org/

  16. Burrows, M., Wheeler, D.J.: A block-sorting lossless data compression algorithm (1994)

    Google Scholar 

  17. Seward, J.: The bzip2 algorithm (2000), http://sources.redhat.com/bzip2

  18. Wallace, G.K.: The jpeg still picture compression standard. Communications of the ACM, 30–44 (1991)

    Google Scholar 

  19. Boutell, T.: Png (portable network graphics) specification version 1.0 (1997)

    Google Scholar 

  20. Schmuck, F.B., Haskin, R.L.: Gpfs: A shared-disk file system for large computing clusters. In: FAST, vol. 2, p. 19 (2002)

    Google Scholar 

  21. Leavitt, N.: Will nosql databases live up to their promise? Computer 43(2), 12–14 (2010)

    Article  Google Scholar 

  22. Copeland, G.P., Khoshafian, S.N.: A decomposition storage model. ACM SIGMOD Record 14(4), 268–279 (1985)

    Article  Google Scholar 

  23. Stonebraker, M., Abadi, D.J., Batkin, A., Chen, X., Cherniack, M., Ferreira, M., Lau, E., Lin, A., Madden, S., O’Neil, E., et al.: C-store: a column-oriented dbms. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 553–564. VLDB Endowment (2005)

    Google Scholar 

  24. Abadi, D.J., Madden, S.R., Hachem, N.: Column-stores vs. row-stores: How different are they really? In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 967–980. ACM (2008)

    Google Scholar 

  25. Crockford, D.: The application/json media type for javascript object notation (json) (2006)

    Google Scholar 

  26. Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. ACM SIGOPS Operating Systems Review 44(2), 35–40 (2010)

    Article  Google Scholar 

  27. Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS) 26(2) (2008)

    Google Scholar 

  28. Auradkar, A., Botev, C., Das, S., De Maagd, D., Feinberg, A., Ganti, P., Gao, L., Ghosh, B., Gopalakrishna, K., Harris, B., et al.: Data infrastructure at linkedin. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 1370–1381. IEEE (2012)

    Google Scholar 

  29. Gupta, P., Goel, A., Lin, J., Sharma, A., Wang, D., Zadeh, R.: Wtf: The who to follow service at twitter. In: Proceedings of the 22nd International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 505–514 (2013)

    Google Scholar 

  30. DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: amazon’s highly available key-value store. In: SOSP, vol. 7, pp. 205–220 (2007)

    Google Scholar 

  31. Cooper, B.F., Ramakrishnan, R., Srivastava, U., Silberstein, A., Bohannon, P., Jacobsen, H.A., Puz, N., Weaver, D., Yerneni, R.: Pnuts: Yahoo!’s hosted data serving platform. Proceedings of the VLDB Endowment 1(2), 1277–1288 (2008)

    Article  Google Scholar 

  32. Chodorow, K.: MongoDB: the definitive guide. O’Reilly (2013)

    Google Scholar 

  33. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  34. White, T.: Hadoop: the definitive guide. O’Reilly (2012)

    Google Scholar 

  35. Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10. IEEE (2010)

    Google Scholar 

  36. George, L.: HBase: the definitive guide. O’Reilly Media, Inc. (2011)

    Google Scholar 

  37. Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., Murthy, R.: Hive: a warehousing solution over a map-reduce framework. Proceedings of the VLDB Endowment 2(2), 1626–1629 (2009)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. MaticMind SpA, CDN Isola F4, Naples, Italy

    Antonio Scarfò

  2. Dept. of Industrial and Information Engineering, Second University of Naples, Via Roma 29, I-81031, Aversa (CE), Italy

    Francesco Palmieri

Authors
  1. Antonio Scarfò
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  2. Francesco Palmieri
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Corresponding author

Correspondence to Antonio Scarfò .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

  2. Fukuoka Institute of Technology (FIT), Fukuoka, Fukuoka, Japan

    Leonard Barolli

  3. University of Salerno, Salerno, Italy

    Admir Barolli

  4. Canadian Institute of Technology, Tirana, Albania

    Petraq Papajorgji

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© 2015 Springer International Publishing Switzerland

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Scarfò, A., Palmieri, F. (2015). How the Big Data Is Leading the Evolution of ICT Technologies and Processes. In: Xhafa, F., Barolli, L., Barolli, A., Papajorgji, P. (eds) Modeling and Processing for Next-Generation Big-Data Technologies. Modeling and Optimization in Science and Technologies, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-09177-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-09177-8_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09176-1

  • Online ISBN: 978-3-319-09177-8

  • eBook Packages: EngineeringEngineering (R0)

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