Big Data: A Survey - The New Paradigms, Methodologies and Tools

Enrico Giacinto Caldarola, Antonio Maria Rinaldi

2015

Abstract

For several years we are living in the era of information. Since any human activity is carried out by means of information technologies and tends to be digitized, it produces a humongous stack of data that becomes more and more attractive to different stakeholders such as data scientists, entrepreneurs or just privates. All of them are interested in the possibility to gain a deep understanding about people and things, by accurately and wisely analyzing the gold mine of data they produce. The reason for such interest derives from the competitive advantage and the increase in revenues expected from this deep understanding. In order to help analysts in revealing the insights hidden behind data, new paradigms, methodologies and tools have emerged in the last years. There has been a great explosion of technological solutions that arises the need for a review of the current state of the art in the Big Data technologies scenario. Thus, after a characterization of the new paradigm under study, this work aims at surveying the most spread technologies under the Big Data umbrella, throughout a qualitative analysis of their characterizing features.

References

  1. Albanese, M., Capasso, P., Picariello, A., and Rinaldi, A. M. (2005). Information retrieval from the web: an interactive paradigm. In Advances in Multimedia Information Systems, pages 17-32. Springer.
  2. Alnafoosi, A. B. and Steinbach, T. (2013). An integrated framework for evaluating big-data storage solutionsida case study. In Science and Information Conference (SAI), 2013, pages 947-956. IEEE.
  3. Amato, F., De Santo, A., Gargiulo, F., Moscato, V., Persia, F., Picariello, A., and Poccia, S. (2015a). Semindex: an index for supporting semantic retrieval of documents. In Proceedings of the IEEE DESWeb ICDE 2015.
  4. Amato, F., De Santo, A., Moscato, V., Persia, F., Picariello, A., and Poccia, S. (2015b). Partitioning of ontologies driven by a structure-based approach. In Semantic Computing (ICSC), 2015 IEEE International Conference on, pages 320-323.
  5. Bhanu, S. (2013). Companies adopting big data analytics to deal with challenges. The Economic Times.
  6. Caldarola, E. G., Picariello, A., and Castelluccia, D. (2015). Modern enterprises in the bubble: Why big data matters. ACM SIGSOFT Software Engineering Notes, 40(1):1-4.
  7. Caldarola, E. G., Sacco, M., and Terkaj, W. (2014). Big data: The current wave front of the tsunami. ACS Applied Computer Science, 10(4):7-18.
  8. Cattell, R. (2011). Scalable sql and nosql data stores. ACM SIGMOD Record, 39(4):12-27.
  9. Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A., and Gruber, R. E. (2008). Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS), 26(2):4.
  10. Chen, M., Mao, S., and Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2):171-209.
  11. Cooper, B. F., Silberstein, A., Tam, E., Ramakrishnan, R., and Sears, R. (2010). Benchmarking cloud serving systems with ycsb. In Proceedings of the 1st ACM Symposium on Cloud Computing, SoCC 7810, pages 143-154, New York, NY, USA. ACM.
  12. Dean, J. and Ghemawat, S. (2008). Mapreduce: Simplified data processing on large clusters. Communications of the ACM, 51(1):107-113.
  13. Desouza, K. C. and Smith, K. L. (2014). Big data for social innovation. Stanford Social Innovation Review.
  14. Dragland, A°. (2013). Big data for better or worse. ScienceDaily.
  15. Euzenat, J., Shvaiko, P., et al. (2007). Ontology matching, volume 18. Springer.
  16. Flouris, G., Plexousakis, D., and Antoniou, G. (2006). A classification of ontology change. In SWAP.
  17. Franks, B. (2012). Taming the big data tidal wave: Finding opportunities in huge data streams with advanced analytics, volume 56. John Wiley & Sons.
  18. Gaber, M. M., Zaslavsky, A., and Krishnaswamy, S. (2005). Mining data streams: a review. ACM Sigmod Record, 34(2):18-26.
  19. Halevy, A., Norvig, P., and Pereira, F. (2009). The unreasonable effectiveness of data. Intelligent Systems, IEEE, 24(2):8-12.
  20. Hey, A. J., Tansley, S., Tolle, K. M., et al. (2009). The fourth paradigm: data-intensive scientific discovery, volume 1. Microsoft Research Redmond, WA.
  21. Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8):36-44.
  22. Jagadish, H., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., and Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7):86-94.
  23. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A. H., and Institute, M. G. (2011). Big data: The next frontier for innovation, competition, and productivity.
  24. Merv, A. (2011). Big data. Teradata Magazine Online, Q1.
  25. Modoni, G., Caldarola, E., Terkaj, W., and Sacco, M. (2015). The knowledge reuse in an industrial scenario: A case study. In eKNOW 2015, The Seventh International Conference on Information, Process, and Knowledge Management, pages 66-71.
  26. Mohanty, S., Jagadeesh, M., and Srivatsa, H. (2013). Big Data Imperatives: Enterprise Big DataWarehouse,BIImplementations and Analytics. Apress.
  27. Rinaldi, A. M. (2008). A content-based approach for document representation and retrieval. In Proceedings of the eighth ACM symposium on Document engineering, pages 106-109. ACM.
  28. Rinaldi, A. M. (2014). A multimedia ontology model based on linguistic properties and audio-visual features. Information Sciences, 277:234-246.
  29. Shvachko, K., Kuang, H., Radia, S., and Chansler, R. (2010). The hadoop distributed file system. In Mass Storage Systems and Technologies (MSST), 2010 IEEE 26th Symposium on, pages 1-10. IEEE.
  30. Van Rijmenam, M. (2014). Think Bigger: Developing a Successful Big Data Strategy for Your Business. AMACOM Div American Mgmt Assn.
  31. Weinberg, B. D., Davis, L., and Berger, P. D. (2013). Perspectives on big data. Journal of Marketing Analytics, 1(4):187-201.
  32. White, T. (2009). Hadoop: the definitive guide. ” O'Reilly Media, Inc.”.
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Paper Citation


in Harvard Style

Caldarola E. and Rinaldi A. (2015). Big Data: A Survey - The New Paradigms, Methodologies and Tools . In Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2015) ISBN 978-989-758-103-8, pages 362-370. DOI: 10.5220/0005580103620370


in Bibtex Style

@conference{komis15,
author={Enrico Giacinto Caldarola and Antonio Maria Rinaldi},
title={Big Data: A Survey - The New Paradigms, Methodologies and Tools},
booktitle={Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2015)},
year={2015},
pages={362-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005580103620370},
isbn={978-989-758-103-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2015)
TI - Big Data: A Survey - The New Paradigms, Methodologies and Tools
SN - 978-989-758-103-8
AU - Caldarola E.
AU - Rinaldi A.
PY - 2015
SP - 362
EP - 370
DO - 10.5220/0005580103620370