Skip to main content
Log in

Comprehensive survey on data warehousing research

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Data, information and knowledge have important role in various human activities because by processing data, information is extracted and by analyzing data and information the knowledge is extracted. The problem of storing, managing and analyzing the huge volumes of data, which is generated regularly by the various sources has been arisen which leads to the need of large data repositories, e.g. data warehouses. In view of the above, a considerable amount attention of research and industry has been attracted by the data warehousing (DW). Various issues and challenges in the field of data warehousing are presented in many studies during the recent years. In this paper, a comprehensive survey is presented to take a holistic view of the research trends in the fields of data warehousing. This paper presents a systematic division of work of researchers in the fields of data warehousing. Finally, current research issues and challenges in the area of data warehousing are summarized for future directions.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Akal F, Böhm K, Schek HJ (2002) OLAP query evaluation in a database cluster: a performance study on intra-query parallelism. In: East-European conf. on advances in databases and information systems (ADBIS), Bratislava, Slovakia

  2. Aleem S, Capretz LF, Ahmed F (2014) Security issues in data warehouse. In: Mastorakis NE, Musić J (eds) Recent advances in information technology. WSEAS Press, pp 15–20

  3. Arora M, Gosain A (2011) Schema evolution for data warehouse: a survey. Int J Comput Appl 22(6):6–14

    Google Scholar 

  4. Arora RK, Gupta MK (2017) e-Governance using data warehousing and data mining. Int J Comput Appl 169(8):28–31

    Google Scholar 

  5. Astriani W, Trisminingsih R (2015) Extraction, transformation, and loading (ETL) module for hotspot spatial data warehouse using Geokettle. In: Procedia, environmental science, Elsevier, the 2nd international symposium on LAPAN-IPB satellite for food security and environmental monitoring 2015, LISAT-FSEM 2015

  6. Chaudhary S, Murala DP, Srivastav VK (2011) A critical review of data warehouse. Glob J Bus Manag Inf Technol 1(2):95–103

    Google Scholar 

  7. Chaudhuri S, Dayal U (1997) An overview of data warehousing and OLAP technology. ACM SIGMOD Rec 26:517–526

    Article  Google Scholar 

  8. Codd EF, Codd SB, Salley CT (1993) Providing OLAP (On-line Analytical Processing) to user-analysts: an IT mandate (white paper)

  9. Dehne F, Robillard D, Rau-Chaplin A, Burke N (2016) VOLAP: a scalable distributed system for real-time OLAP with high velocity data. In: 2016 IEEE international conference on cluster computing (CLUSTER). IEEE, pp 354–363

  10. ElGamal N, El-Bastawissy A, Galal-Edeen GH (2016) An architecture-oriented data warehouse testing approach. In: COMAD, pp 24–34

  11. Furtado P (2009) A survey on parallel and distributed data warehouses. Int J Data Warehouse Min 5(2):57–77

    Article  Google Scholar 

  12. Geary N, Jarvis B, Mew C, Gore H, Precisionpoint Software Limited (2017) Method and apparatus for automatically creating a data warehouse and OLAP cube. US Patent 9,684,703

  13. Golfarelli M, Rizzi S (2009) A comprehensive approach to data warehouse testing. In: ACM, DOLAP’09, Hong Kong, China, November 6, 2009

  14. Golfarelli M, Rizzi S (2018) From star schemas to big data: 20+ years of data warehouse research. In: A comprehensive guide through the Italian database research over the last 25 years. Springer International Publishing, pp 93–107

  15. Gosain A, Heena (2015) Literature review of data model quality metrics of data warehouse. In: Procedia, computer science, Elsevier, international conference on intelligent computing, communication and convergence (ICCC-2014)

  16. Gupta A, Harinarayan V, Quass D (1995) Aggregate-query processing in data warehousing environment. In: Proc. 21st int. conf. very large data bases, pp 358–369, Zurich, Switzerland, Sept. 1995

  17. Gupta SL, Mathur S, Schema P (2012) Data warehouse vulnerability and security. Int J Sci Eng Res 3(5):1–5

    Google Scholar 

  18. Haertzen D (2009) Testing the data warehouse. http://www.infogoal.com

  19. Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. Elsevier

  20. Hurtado CA, Gutierrez C, Mendelzon AO (2005) Capturing summarizability with integrity constraints in OLAP. ACM Trans Database Syst 30(3):854–886

    Article  Google Scholar 

  21. Inmon WH (2005) Building the data warehouse, 5th edn. Wiley, New York

    Google Scholar 

  22. Jaiswal A (2014) Security measures for data warehouse. Int J Sci Eng Technol Res 3(6):1729–1733

    Google Scholar 

  23. Jindal R, Taneja S (2012) Comparative study of data warehouse design approaches: a survey. Int J Database Manag Syst (IJDMS) 4(1):33–45

    Article  Google Scholar 

  24. Kuijpers B, Gomez L, Vaisman A (2017) Performing OLAP over graph data: query language, implementation, and a case study. In: BIRTE '17 proceedings of the international workshop on real-time business intelligence and analytics, no 6. ACM, New York

  25. Kumar S, Singh B, Kaur G (2016) Data warehouse security issue. Int J Adv Res Comput Sci 7(6):177–179

    Google Scholar 

  26. Mathen MP (2010) Data warehouse testing. Infosys White Paper, Mar 2010

  27. Mookerjea A, Malisetty P (2008) Best practices in data warehouse testing. In: Proc. test, New Delhi, 2008

  28. O’Neil P, Graefe G (1995) Multi-table joins through bitmapped join indices. SIGMOD Rec 24(3):8–11

    Article  Google Scholar 

  29. Oliveira B, Belo O (2015) A domain-specific language for ETL patterns specification in data warehousing systems. In: Chapter in progress in artificial intelligence, Springer, Volume 9273 of the series lecture notes in computer science, pp 597–602

  30. Oracle Corporation (2005) Oracle advanced security transparent data encryption best practices. Oracle White Paper, July 2010

  31. Oueslati W, Akaichi J (2010) A survey on data warehouse evolution. Int J Database Manag Syst (IJDMS) 2(4):11–24

    Article  Google Scholar 

  32. Ponniah P (2001) Data warehousing fundamentals. Wiley, New York

    Book  Google Scholar 

  33. Rizzi S, Golfarelli M (1999) A methodological framework for data warehouse design. DOLAP 98 Washington DC USA, Copyright ACM, l-581 13-120-8/98/l 1

  34. Rousopoulos R (1998) Materialized views and data warehouses. SIGMOD Rec 27(1):21–26

    Article  Google Scholar 

  35. Santos RJ, Bernardino J, Vieira M (2011) A survey on data security in data warehousing: issues, challenges and opportunities. In: EUROCON-International Conference on Computer as a Tool (EUROCON), 2011 IEEE, Print ISBN: 978-1-4244-7486-8

  36. Taktak S, Alshomrani S, Feki J, Zurfluh G (2017) The power of a model-driven approach to handle evolving data warehouse requirements. In: MODELSWARD, pp 169–181

  37. Tang B, Han S, Yiu ML, Ding R, Zhang D (2017) Extracting top-k insights from multi-dimensional data. In: Proceedings of the 2017 ACM international conference on management of data. ACM, pp 1509–1524

  38. Trujillo J, Palomar M, Gómez J, Song IY (2001) Designing data warehouses with OO conceptual models. IEEE Comput 34(12):66–75

    Article  Google Scholar 

  39. Vassiliadis P, Sellis T (1999) A survey of logical models for OLAP databases. SIGMOD Rec 28(4):64–69

    Article  Google Scholar 

  40. Venkatadri M, Reddy LC (2011) A review on data mining from Past to the Future. Int J Comput Appl 15(7):19–22

    Google Scholar 

  41. Vishnu B, Manjunath TN, Hamsa C (2014) An effective data warehouse security framework. Int J Comput Appl Recent Adv Inf Technol 33–37

  42. Wang Z, Chu Y, Tan KL, Agrawal D, Abbadi AE (2016) HaCube: extending MapReduce for efficient OLAP cube materialization and view maintenance. In: International conference on database systems for advanced applications. Springer, Cham, pp 113–129

  43. Yangui R, Nabli A, Gargouri F (2016) Automatic transformation of data warehouse schema to NoSQL data base: comparative study. In: Procedia, computer science, Elsevier, 20th international conference on knowledge based and intelligent information and engineering systems, KES2016, 5–7 September 2016, York, UK

  44. Zeng K, Agarwal S, Stoica I (2016) IOLAP: managing uncertainty for efficient incremental OLAP. In: Proceedings of the 2016 international conference on management of data. ACM, pp 1347–1361

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manoj K. Gupta.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chandra, P., Gupta, M.K. Comprehensive survey on data warehousing research. Int. j. inf. tecnol. 10, 217–224 (2018). https://doi.org/10.1007/s41870-017-0067-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-017-0067-y

Keywords

Navigation