Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

NOSQL Design for Analytical Workloads: Variability Matters

  • Conference paper
  • First Online:
Conceptual Modeling (ER 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9974))

Included in the following conference series:

Abstract

Big Data has recently gained popularity and has strongly questioned relational databases as universal storage systems, especially in the presence of analytical workloads. As result, co-relational alternatives, commonly known as NOSQL (Not Only SQL) databases, are extensively used for Big Data. As the primary focus of NOSQL is on performance, NOSQL databases are directly designed at the physical level, and consequently the resulting schema is tailored to the dataset and access patterns of the problem in hand. However, we believe that NOSQL design can also benefit from traditional design approaches. In this paper we present a method to design databases for analytical workloads. Starting from the conceptual model and adopting the classical 3-phase design used for relational databases, we propose a novel design method considering the new features brought by NOSQL and encompassing relational and co-relational design altogether.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://hive.apache.org.

  2. 2.

    https://spark.apache.org.

  3. 3.

    https://www.oracle.com/database.

  4. 4.

    https://hbase.apache.org.

References

  1. Alagiannis, I., et al.: H2O: a hands-free adaptive store. In: SIGMOD (2014)

    Google Scholar 

  2. Ambler, S.: Agile Database Techniques: Effective Strategies for the Agile Software Developer. Wiley, New York (2003)

    Google Scholar 

  3. Blaha, M.: On reverse engineering of vendor databases. In: WCRE (1998)

    Google Scholar 

  4. Blaha, M.: Patterns of Data Modeling. CRC Press, Inc., Boca Raton (2010)

    Google Scholar 

  5. Bondiombouy, C., Kolev, B., Levchenko, O., Valduriez, P.: Integrating big data and relational data with a functional SQL-like query language. In: Databaseand Expert Systems Applications - 26th International Conference, DEXA 2015, Valencia, Spain, 1–4 September 2015, Proceedings, Part I, pp. 170–185 (2015). http://dx.doi.org/10.1007/978-3-319-22849-5_13

    Google Scholar 

  6. Bugiotti, F., Cabibbo, L., Atzeni, P., Torlone, R.: Database design for NoSQL systems. In: Yu, E., Dobbie, G., Jarke, M., Purao, S. (eds.) ER 2014. LNCS, vol. 8824, pp. 223–231. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12206-9_18

    Google Scholar 

  7. Garcia, S., et al.: DSS from an RE perspective: a systematic mapping. J. Syst. Softw. 117, 488–507 (2016)

    Article  Google Scholar 

  8. Garcia-Molina, H., et al.: Database Systems - The Complete Book. Pearson Education, Harlow (2009)

    Google Scholar 

  9. Gartner: Focus on the ’Three Vs’ of Big Data Analytics: Variability, Veracity and Value. https://www.gartner.com/doc/2921417/focus-vs-big-data-analytics

  10. Inmon, W.H., et al.: Corporate Information Factory. Wiley, New York (2001)

    Google Scholar 

  11. Jagadish, H.V., et al.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)

    Article  Google Scholar 

  12. Kimball, R.: The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. Wiley, New York (1996)

    Google Scholar 

  13. Mazón, J.-N., Trujillo, J., Lechtenbörger, J.: A set of QVT relations to assure the correctness of data warehouses by using multidimensional normal forms. In: Embley, D.W., Olivé, A., Ram, S. (eds.) ER 2006. LNCS, vol. 4215, pp. 385–398. Springer, Heidelberg (2006). doi:10.1007/11901181_29

    Chapter  Google Scholar 

  14. Meijer, E., Bierman, G.M.: A co-relational model of data for large shared data banks. Commun. ACM 54(4), 49–58 (2011)

    Article  Google Scholar 

  15. OCDE: Data-driven Innovation for Growth and Well-being. http://www.oecd.org/sti/inno/data-driven-innovation-interim-synthesis.pdf

  16. Özsu, M.T., Valduriez, P.: Principles of Distributed DB Systems. Springer, New York (2011)

    Google Scholar 

  17. Romero, O., et al.: Tuning small analytics on big data: data partitioning and secondary indexes in the Hadoop ecosystem. Inf. Syst. 54, 336–356 (2015)

    Article  Google Scholar 

  18. Sadalage, P., Fowler, M.: NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley Professional, Upper Saddle River (2012)

    Google Scholar 

  19. Stonebraker, M.: What Does ‘Big Data’ Mean? http://cacm.acm.org/blogs/blog-cacm/155468-what-does-big-data-mean/fulltext

  20. Stonebraker, M., et al.: C-store: a column-oriented DBMS. In: VLDB (2005)

    Google Scholar 

  21. TDWI: TDWI Best Practices Report, Achieving Greater Agility with Business Intelligence. https://tdwi.org/research/2013/01/tdwi-best-practices-report-achieving-greater-agility-with-business-intelligence.aspx

  22. Wiese, L.: Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. DeGruyter, Boston (2015)

    Google Scholar 

Download references

Acknowledgments

We would like to thank Antoni Olivé for revising the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Victor Herrero .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Herrero, V., Abelló, A., Romero, O. (2016). NOSQL Design for Analytical Workloads: Variability Matters. In: Comyn-Wattiau, I., Tanaka, K., Song, IY., Yamamoto, S., Saeki, M. (eds) Conceptual Modeling. ER 2016. Lecture Notes in Computer Science(), vol 9974. Springer, Cham. https://doi.org/10.1007/978-3-319-46397-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46397-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46396-4

  • Online ISBN: 978-3-319-46397-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics