Advertisement

Big-Data – Theoretical, Engineering and Analytics Perspective

  • Vijay Srinivas Agneeswaran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7678)

Abstract

The advent of social networks, increasing speed of computer networks, the increasing processing power (through multi-cores) has given enterprise and end users the ability to exploit big-data. The focus of this tutorial is to explore some of the fundamental trends that led to the Big-Data hype (reality) as well as explain the analytics, engineering and theoretical trends in this space.

Keywords

Normal Operation Software Define Network Analytics Perspective Hadoop Distribute File System Erasure Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abadi, D.J.: Consistency Tradeoffs in Modern Distributed Database System Design: CAP is Only Part of the Story. Computer 45(2), 37–42 (2012)MathSciNetCrossRefGoogle Scholar
  2. Baker, J.: Megastore: Providing Scalable, Highly Available Storage for Interactive Service. In: Conference on Innovative Data Systems Research, CIDR (2011)Google Scholar
  3. He, B., Hsiao, H.-I.: Efficient Iceberg Query Evaluation Using Compressed Bitmap Index. IEEE Transactions on Data and Knowledge Engineering 24(9), 1570–1583 (2012)CrossRefGoogle Scholar
  4. Cooper, B.F., Ramakrishnan, R.: PNUTS: Yahoo!’s Hosted Data Serving Platform. Proceedings of VLDB Endowment, 1277–1288 (2008)Google Scholar
  5. Burrows, M.: The Chubby Lock Service for Loosely-coupled Distributed Systems. In: ACM Symposium on Operating System Design and Implementation (OSDI), pp. 335–350. ACM (2007)Google Scholar
  6. Huang, C., Simitci, H.: Erasure Coding in Windows Azure Storage. In: USENIX Conference on Annual Technical Conference (USENIX ATC 2012), p. 2. USENIX Association (2012)Google Scholar
  7. Dirolf, M., Chodorow, K.: MongoDB: The Definitive Guide, 1st edn. O’Reilly Media, Inc. (2010)Google Scholar
  8. Chang, F., Dean, J.: Bigtable: A Distributed Storage System for Structured Data. In: 7th USENIX Symposium on Operating Systems Design and Implementation, p. 15. USENIX Association, Berkeley (2006)Google Scholar
  9. DeCandia, G., Hastorun, D.: Dynamo: Amazon’s highly available key-value store. In: ACM Symposium on Operating Systems Principles, pp. 205–220. ACM (2007)Google Scholar
  10. Malewicz, G., Matthew, H.: Pregel: A System for Large-Scale Graph Processing. In: SIGMOD International Conference on Management of Data (SIGMOD 2010), pp. 135–146. ACM, NY (2010)CrossRefGoogle Scholar
  11. Lindsay, B.S.: Single and Multi-Site Recovery Facilities. In: Poole, I.W. (ed.) Distributed Databases. Cambridge University Press (1980)Google Scholar
  12. Lynch, N., Gilbert, S.: Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News (June 2002)Google Scholar
  13. Fischer, M.J., Lynch, N.A.: Impossibility of Distributed Consensus with one Faulty Process. Journal of the ACM 32(2), 374–382 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  14. Malik, P., Lakshman, A.: Cassandra: A Decentralized Structured Storage System. SIGOPS Operating Systems Review 44(2), 35–40 (2010)CrossRefGoogle Scholar
  15. Meyer, M.: The Riak Handbook (2012), http://riakhandbook.com
  16. Ramakrishnan, R.: CAP and Cloud Data Management. Computer 45(2), 43–49 (2012)CrossRefGoogle Scholar
  17. Sumbaly, R., Kreps, J.: Serving Large-scale Batch Computed Data with Project Voldemort. In: 10th USENIX Conference on File and Storage Technologies (FAST 2012), p. 18. USENIX Association, Berkeley (2012)Google Scholar
  18. Al-Kiswany, S., Gharaibeh, A., Santos-Neto, E.: StoreGPU: Exploiting Graphics Processing Units to Accelerate Distributed Storage Systems. In: 17th International Symposium on High Performance Distributed Computing (HPDC 2008), pp. 165–174. ACM, NY (2008)CrossRefGoogle Scholar
  19. Melnik, S., Gubarev, A.: Dremel: Interactive Analysis of Web-Scale Datasets. Communications of the ACM 54(6), 114–123 (2011)CrossRefGoogle Scholar
  20. Stonebraker, M.: Volt DB Blogs (2012), http://voltdb.com
  21. Chandra, T.D., Griesemer, R.: Paxos Made Live: An Engineering Perspective. In: Twenty-Sixth Annual ACM Symposium on Principles of Distributed Computing (PODC 2007), pp. 398–407. ACM (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vijay Srinivas Agneeswaran
    • 1
  1. 1.Innovation LabsImpetus Infotech (India) Pvt Ltd.BangaloreIndia

Personalised recommendations