Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Data Warehousing on Nonconventional Data

  • Rafael Berlanga
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80670

Definition

Non-conventional data are any kind of data that are useful for business intelligence (BI) but that cannot be directly managed with traditional data warehousing (DW) technology. Non-conventional data cover a great variety of user-generated contents such as domain knowledge, corporate documents like contracts and e-mails, news feeds, messages posted on social media, and so on. Non-conventional data sources have in common a semi-structured, dynamic, and text-rich nature, which make difficult their integration within traditional corporate information systems (including data warehouses). Nowadays, non-conventional data mainly reside in the Web, adopting the standard formats proposed by the World Wide Web Consortium (W3C), like HTML, XML, RSS, RDF, etc. This entry is focused on those approaches aimed to either integrate non-conventional data with traditional DW/OLAP or to perform ad hoc DW of these data sources. This entry does not account for approaches that adopt Web languages...

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

Recommended Reading

  1. 1.
    Abelló A, Romero O, Pedersen TB, Berlanga R, Nebot V, Aramburu MJ, Simitsis A. Using semantic web technologies for exploratory OLAP: a survey. IEEE Trans Knowl Eng. 2015;27(2):571–588.CrossRefGoogle Scholar
  2. 2.
    Bhide MA, Gupta A, Gupta R, Roy P, Mohania MK, Ichhaporia Z. LIPTUS: associating structured and unstructured information in a banking environment. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data; 2007. p. 915–24.Google Scholar
  3. 3.
    Castellanos M, Wang S, Dayal U, Gupta C. SIE-OBI: a streaming information extraction platform for operational business intelligence. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data; 2010. p. 1105–10.Google Scholar
  4. 4.
    Chakaravarth, VT, Gupta H, Roy P, Mohania M. Efficiently linking text documents with relevant structured information. In: Proceedings of the 32nd International Conference on Very Large Data Bases; 2006. p. 667–78.Google Scholar
  5. 5.
    Francia M, Golfarelli M, Rizzi S. A methodology for social BI. In: Proceedings of the 18th International Database Engineering & Applications Symposium; 2014. p. 207–16.Google Scholar
  6. 6.
    García-Moya L, Kudama S, Aramburu MJ, Berlanga R. Storing and analysing voice of the market data in the corporate data warehouse. Inf Syst Front. 2013;15(3):331–49.CrossRefGoogle Scholar
  7. 7.
    Löser A, Nagel C, Pieper S, Boden C. Beyond search: retrieving complete tuples from a text-database. Inf Syst Front. 2013;15(3):311–29.CrossRefGoogle Scholar
  8. 8.
    Mothe J, Chrisment C, Dousset B, Alaux J. DocCube: multi-dimensional visualisation and exploration of large document sets. J Am Soc Inf Sci Technol. 2003;54(7):650–9.CrossRefGoogle Scholar
  9. 9.
    Nebot V, Berlanga R. Building data warehouses with semantic web data. Decis Support Syst. 2012;52(4):853–68.CrossRefGoogle Scholar
  10. 10.
    Park B, Song I. Incorporating text OLAP in business intelligence. In: Zorrilla M, Mazón J, Ferrández Ó, Garrigós I, Daniel F, Trujillo J, editors. Business intelligence applications and the web: models, systems and technologies. Hershey: IGI Global; 2012.p. 77–101.Google Scholar
  11. 11.
    Pérez JM, Berlanga R, Aramburu MJ, Pedersen TB. Contextualizing data warehouses with documents. Decis Support Syst. 2008;45(1):77–94.CrossRefGoogle Scholar
  12. 12.
    Pérez JM, Berlanga R, Aramburu MJ, Pedersen TB. Integrating data warehouses with web data: a survey. IEEE Trans Knowl Data Eng. 2008;20(7):940–55.CrossRefGoogle Scholar
  13. 13.
    Simitsis A, Baid A, Sismanis Y, Reinwald B. Multidimensional content exploration. Proc VLDB Endow. 2008;1(1):660–71.CrossRefGoogle Scholar
  14. 14.
    Tseng FSC, Chou AYH. The concept of document warehousing for multi-dimensional modeling of textual-based business intelligence. Decis Support Syst. 2006;42(2):727–44.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Languages and SystemsUniversitat Jaume ICastellónSpain

Section editors and affiliations

  • Torben Bach Pedersen
    • 1
  • Stefano Rizzi
    • 2
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.DISIUniversity of BolognaBolognaItaly