Skip to main content

Active, Real-Time, and Intellective Data Warehousing

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

Synonyms

Right-time data warehousing [24]

Definition

Active data warehousing is the technical ability to capture transactions when they change and integrate them into the warehouse, along with maintaining batch or scheduled cycle refreshes. An active data warehouse offers the possibility of automating routine tasks and decisions. The active data warehouse exports decisions automatically to the online transaction processing (OLTP) systems.

Real-time data warehousing describes a system that reflects the state of the source systems in real time. If a query is run against the real-time data warehouse to understand a particular facet about the business or entity described by the warehouse, the answer reflects the fully up-to-date state of that entity. Most data warehouses have data that are highly latent and thus reflect the business at a point in the past. In contrast, a real-time data warehouse has low latency data and provides current (or real-time) data.

Simply put, a real-time data...

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

Access this chapter

Institutional subscriptions

Recommended Reading

  1. Kimball R, Strethlo K. Why decision support fails and how to fit it. ACM SIGMOD Rec. 1995;24(3):91–7.

    Article  Google Scholar 

  2. Golfarelli M, Maio D, Rizzi S. Conceptual design of data warehouses from E/R schemes. In: Proceedings of the 31st Annual Hawaii International Conference on System Sciences, Vol. VII; 1998. p. 334–43.

    Google Scholar 

  3. Lehner W. Modeling large scale OLAP scenarios. In: Advances in database technology. In: Proceedings of the 6th International Conference on Extending Database Technology; 1998. p. 153–67.

    Google Scholar 

  4. Samtani S, Mohania M, Kumar V, Kambayashi Y. Recent advances and research problems in data warehousing. In: ER ‘98 Proceedings of the Workshops on Data Warehousing and Data Mining: Advances in Database Technologies; 1998. p. 81–92.

    Google Scholar 

  5. Pedersen TB, Jensen CS. Multidimensional data modeling for complex data. In: Proceedings of the 15th International Conference on Data Engineering; 1999. p. 336–45.

    Google Scholar 

  6. Vassiliadis P. Modeling multidimensional databases, cubes and cube operations. In: Proceedings of the 10th International Conference on Scientific and Statistical Database Management; 1998. p. 53–62.

    Google Scholar 

  7. Mohania M, Samtani S, Roddick J, Kambayashi Y. Advances and research directions in data-warehousing technology. Australas J Inf Syst. 1999;7:1.

    Google Scholar 

  8. Thalhammer T, Schrefl M, Mohania M. Active data warehouses: complementing OLAP with analysis rules. Data Knowl Eng. 2001;39(3):241–69.

    Article  MATH  Google Scholar 

  9. ACT-NET Consortium. The active database management system manifesto: a rulebase of ADBMS features. ACM SIGMOD Rec. 1996;25(3).

    Google Scholar 

  10. Simon E, Dittrich A. Promises and realities of active database systems. In: Proceedings of the 21th International Conference on Very Large Data Bases; 1995. p. 642–53.

    Google Scholar 

  11. Brobst S. Active data warehousing: a new breed of decision support. In: Proceedings of the 13th International Workshop on Data and Expert System Applications; 2002. p. 769–72.

    Google Scholar 

  12. Borbst S, Rarey J. The five stages of an active data warehouse evolution. Teradata Mag. 2001;3:38–44.

    Google Scholar 

  13. IBM Data Warehousing. https://www.ibm.com/analytics/us/en/data-management/data-warehouse.

  14. Best practices for Real-time Data Warehousing. An oracle white paper. 2014. http://www.oracle.com/us/products/middleware/data-integration/realtime-data-warehousing-bp-2167237.pdf

  15. Kimball R, Caserta J. The data warehouse ETL toolkit: practical techniques for extracting, cleaning, conforming, and delivering data. Wiley; 2004.

    Google Scholar 

  16. Linthicum RS. Enterprise application integration. Addison-Wesley; 1999.

    Google Scholar 

  17. Improving SOA with Goldengate TDM Technology. GoldenGate White Paper; 2007.

    Google Scholar 

  18. Langseth J. Real-time data warehousing: challenges and solutions. DSSResources.COM; 2004.

    Google Scholar 

  19. Paton NW, Diaz O. Active database systems. ACM Comput Surv. 31, 1999;1.

    Google Scholar 

  20. High R. The era of cognitive systems: an inside look at IBM Watson and how it works. IBM Corporation Redbooks; 2012.

    Google Scholar 

  21. Abadi D, Agrawal R, Ailamaki A, Balazinska M, Bernstein P, Carey M, Chaudhuri S, Dean J, Doan A, Franklin M, Gehrke J, Haas L, Halevy A, Hellerstein J, Ioannidis Y, Jagadish H, Kossmann D, Madden S, Mehrotra S, Milo T, Naughton J, Ramakrishnan R, Markl V, Olston C, Ooi BC, Re C, Suciu D, Stonebraker M, Walter T, Widom J. The Beckman report on database research. Commun ACM. 2016;59(2):92–9.

    Article  Google Scholar 

  22. Jonas J, Sokol L. Data finds data, Chapter 7. In: Segaran T, Hammerbacher J, editors. Beautiful data: the stories behind elegant data solutions. O’Reilly Media; 2009.

    Google Scholar 

  23. Chirigati F, Liu J, Korn F, Wu YW, Yu C, Zhang H. Knowledge exploration using tables on the web. PVLDB. 2016;10(3):193–204.

    Google Scholar 

  24. Thomsen C, Pedersen TB, Lehner W. RiTE: providing on-demand data for right-time data warehousing. In: Proceedings of the of the IEEE 24th International Conference on Data Engineering (ICDE); 2008. p. 456–65.

    Google Scholar 

  25. Agrawal D. The reality of real-time business intelligence. In: Castellanos M, Dayal U, Sellis T, editors. Proceedings of the 2nd International Workshop on Business Intelligence for the Real-Time Enterprise (BIRTE 2008). Springer, LNBIP; 2009. 27, 75–88.

    Google Scholar 

  26. Cao Y., Chen C., Guo F., Jiang D., Lin Y., Ooi B. C., Vo H. T., Wu S., Xu Q. ES2: a cloud data storage system for supporting both OLTP and OLAP. In: ICDE; 2011. p. 291–302.

    Google Scholar 

  27. Kemper A, Neumann T. HyPer: a hybrid OLTP and OLAP main memory database system based on virtual memory snapshots. In: ICDE; 2011. p. 195–206.

    Google Scholar 

  28. Sikka V, Färber F, Lehner W, Cha SK, Peh T, Bornhövd C. Efficient transaction processing in SAP HANA database: the end of a column store myth. In: SIGMOD; 2012. p. 731–42.

    Google Scholar 

  29. SAP HANA data warehousing. 2015. http://www.computerweekly.com/feature/SAP-Hana-as-a-data-warehouse

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mukesh Mohania .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media LLC

About this entry

Cite this entry

Mohania, M., Nambiar, U., Vo, H.T., Schrefl, M., Vincent, M. (2018). Active, Real-Time, and Intellective Data Warehousing. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_8-3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_8-3

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7993-3

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics