[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Data Cube Is Dead, Long Life to Data Cube in the Age of Web Data

  • Conference paper
  • First Online:
Big Data Analytics (BDA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11932))

Included in the following conference series:

Abstract

In a short time, the data warehouse (DW) technology took an important place in the academic and industrial landscapes. This place materialized in the large majority of engineering and management schools that adopted it in their curriculum and in the small, medium-size and large companies that enhanced their decision making capabilities thanks to it. The 1990s saw the advent of conferences such as DaWaK and DOLAP that carried the acronyms DW and OLAP in their titles. Then, all of a sudden, this technology has been upset by the arrival of Big Data. Consequently, those actors have replaced DW and OLAP by Big Data Analytics. We are well placed to assert that this brutal move may have a negative impact on schools, academia, and industry. This technology is not dead, today’s context, with the connected world and Web of Data, is more favorable than when building DW merely stemmed from company internal sources. In this invited paper, we attempt to answer the following question: how does DW technology interact with Linked Open Data (LOD)? To answer the question, we provide a complete vision to augment the traditional DW with LOD, to capture and quantify the added value generated through this interaction. This vision covers the main steps of the DW life-cycle. This value is estimated through two different perspectives: (i) a source-oriented vision, by calculating the rate of the DW augmentation in terms of multidimensional concepts and instances, and (ii) a goal-oriented vision where the value is calculated according to the ability of the DW to estimate the performance levels of defined goals that reflect the strategy of a company, using the defined DW of the case study of a leading Algerian company.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • 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://www.mordorintelligence.com/industry-reports/global-active-data-warehousing-market-industry.

  2. 2.

    https://datavirtuality.com/blog-calculating-the-return-on-investment-roi-of-business-intelligence-projects/.

  3. 3.

    https://developer.ibm.com/tutorials/ba-augment-data-warehouse1/.

  4. 4.

    https://www.europeandataportal.eu/elearning/en/module1/#/id/co-01.

  5. 5.

    http://wiki.dbpedia.org/.

References

  1. Abelló Gamazo, A., Gallinucci, E., Golfarelli, M., Rizzi Bach, S., Romero Moral, O.: Towards exploratory OLAP on linked data. In: SEBD, pp. 86–93 (2016)

    Google Scholar 

  2. Baldacci, L., Golfarelli, M., Graziani, S., Rizzi, S.: QETL: an approach to on-demand ETL from non-owned data sources. DKE 112, 17–37 (2017)

    Article  Google Scholar 

  3. Barone, D., Jiang, L., Amyot, D., Mylopoulos, J.: Reasoning with key performance indicators. In: Johannesson, P., Krogstie, J., Opdahl, A.L. (eds.) PoEM 2011. LNBIP, vol. 92, pp. 82–96. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24849-8_7

    Chapter  Google Scholar 

  4. Bellatreche, L.: Value-driven approach for BI application design. Dagstuhl Reports: Next Generation Domain Specific Conceptual Modeling: Principles and Methods (Dagstuhl Seminar 18471), vol. 8, no. 11, p. 69 (2019)

    Google Scholar 

  5. Berkani, N., Bellatreche, L., Benatallah, B.: A value-added approach to design BI applications. In: Madria, S., Hara, T. (eds.) DaWaK 2016. LNCS, vol. 9829, pp. 361–375. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43946-4_24

    Chapter  Google Scholar 

  6. Berkani, N., Bellatreche, L., Guittet, L.: ETL processes in the era of variety. In: Hameurlain, A., Wagner, R., Benslimane, D., Damiani, E., Grosky, W.I. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX. LNCS, vol. 11310, pp. 98–129. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-58415-6_4

    Chapter  Google Scholar 

  7. Berkani, N., Bellatreche, L., Khouri, S., Ordonez, C.: Value-driven approach for designing extended data warehouses. In: DOLAP (2019)

    Google Scholar 

  8. Božič, K., Dimovski, V.: Business intelligence and analytics for value creation: the role of absorptive capacity. IJIM 46, 93–103 (2019)

    Google Scholar 

  9. Chakrabarti, S., Sarawagi, S., Dom, B.: Mining surprising patterns using temporal description length. In: VLDB, pp. 606–617 (1998)

    Google Scholar 

  10. Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)

    Article  Google Scholar 

  11. Corrales-Garay, D., Mora-Valentín, E., Ortiz-de-Urbina-Criado, M.: Open data for open innovation: an analysis of literature characteristics. Futur. Internet 11(3), 77–102 (2019)

    Article  Google Scholar 

  12. Deb Nath, R.P., Hose, K., Pedersen, T.B.: Towards a programmable semantic extract-transform-load framework for semantic data warehouses. In: DOLAP, pp. 15–24 (2015)

    Google Scholar 

  13. Dehdouh, K.: Building OLAP cubes from columnar NoSQL data warehouses. In: Bellatreche, L., Pastor, Ó., Almendros Jiménez, J.M., Aït-Ameur, Y. (eds.) MEDI 2016. LNCS, vol. 9893, pp. 166–179. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45547-1_14

    Chapter  Google Scholar 

  14. Domingues, M.A., Jorge, A.M., Soares, C., Leal, J.P., Machado, P.: A data warehouse for web intelligence. In: 13th Portuguese Conference on Artificial Intelligence (EPIA), pp. 487–499 (2007)

    Google Scholar 

  15. Gallinucci, E., Golfarelli, M., Rizzi, S., Abelló, A., Romero, O.: Interactive multidimensional modeling of linked data for exploratory OLAP. Inf. Syst. 77, 86–104 (2018)

    Article  Google Scholar 

  16. Golfarelli, M., Maio, D., Rizzi, S.: The dimensional fact model: a conceptual model for data warehouses. Int. J. Cooper. Inf. Syst. 7(02n03), 215–247 (1998)

    Article  Google Scholar 

  17. Golfarelli, M., Rizzi, S., Vrdoljak, B.: Data warehouse design from XML sources. In: ACM OLAP, pp. 40–47 (2001)

    Google Scholar 

  18. Gordijn, J., Akkermans, J.: Value-based requirements engineering: exploring innovative e-commerce ideas. Requir. Eng. 8(2), 114–134 (2003)

    Article  Google Scholar 

  19. Guarino, N., Andersson, B., Johannesson, P., Livieri, B.: Towards an ontology of value ascription. In: FOIS, pp. 331–344 (2016)

    Google Scholar 

  20. Hoffart, J., et al.: YAGO2: exploring and querying world knowledge in time, space, context, and many languages. In: WWW, pp. 229–232 (2011)

    Google Scholar 

  21. Horkoff, J., et al.: Strategic business modeling: representation and reasoning. SSM 13(3), 1015–1041 (2014)

    Google Scholar 

  22. Khouri, S., Ghomari, A.R., Aouimer, Y.: Thinking the incorporation of LOD in semantic cubes as a strategic decision. In: Schewe, K.-D., Singh, N.K. (eds.) MEDI 2019. LNCS, vol. 11815, pp. 287–302. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32065-2_20

    Chapter  Google Scholar 

  23. Khouri, S., Lanasri, D., Saidoune, R., Boudoukha, K., Bellatreche, L.: LogLInc: LoG queries of linked open data investigator for cube design. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DEXA 2019. LNCS, vol. 11706, pp. 352–367. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27615-7_27

    Chapter  Google Scholar 

  24. Khouri, S., Semassel, K., Bellatreche, L.: Managing data warehouse traceability: a life-cycle driven approach. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 199–213. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_13

    Chapter  Google Scholar 

  25. Larson, D., Chang, V.: A review and future direction of agile, business intelligence, analytics and data science. Int. J. Inf. Manag. 36(5), 700–710 (2016)

    Article  Google Scholar 

  26. LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21 (2011)

    Google Scholar 

  27. Llave, M.R.: Data lakes in business intelligence: reporting from the trenches. Proc. Comput. Sci. 138, 516–524 (2018)

    Article  Google Scholar 

  28. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D., Barton, D.: Big data: the management revolution. Harv. Bus. Rev. 90(10), 60–68 (2012)

    Google Scholar 

  29. Mithas, S., Lee, M.R., Earley, S., Murugesan, S., Djavanshir, R.: Leveraging big data and business analytics. IT Prof. 15(6), 18–20 (2013)

    Article  Google Scholar 

  30. Nebot, V., Berlanga, R.: Building data warehouses with semantic data. In: Proceedings of the 2010 EDBT/ICDT Workshops, p. 9. ACM (2010)

    Google Scholar 

  31. Rehman, N.U., Weiler, A., Scholl, M.H.: OLAPing social media: the case of Twitter. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1139–1146 (2013)

    Google Scholar 

  32. Rizzi, S., Gallinucci, E., Golfarelli, M., Abelló, A., Romero, O.: Towards exploratory OLAP on linked data. In: SEBD, pp. 86–93 (2016)

    Google Scholar 

  33. Sales, T.P., Baião, F., Guizzardi, G., Almeida, J.P.A., Guarino, N., Mylopoulos, J.: The common ontology of value and risk. In: Trujillo, J.C., et al. (eds.) ER 2018. LNCS, vol. 11157, pp. 121–135. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00847-5_11

    Chapter  Google Scholar 

  34. Sales, T.P., Guarino, N., Guizzardi, G., Mylopoulos, J.: An ontological analysis of value propositions. In: EDOC, pp. 184–193 (2017)

    Google Scholar 

  35. Silva Souza, V.E., Mazon, J.N., Garrigos, I., Trujillo, J., Mylopoulos, J.: Monitoring strategic goals in data warehouses with awareness requirements. In: ACM SAC, pp. 10–75 (2012)

    Google Scholar 

  36. Thew, S., Sutcliffe, A.: Value-based requirements engineering: method and experience. Requir. Eng. 23(4), 443–464 (2018)

    Article  Google Scholar 

  37. Vrdoljak, B., Banek, M., Rizzi, S.: Designing web warehouses from XML schemas. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 89–98. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45228-7_10

    Chapter  Google Scholar 

  38. Wegmann, A.: On the systemic enterprise architecture methodology (SEAM). In: ICEIS, pp. 483–490 (2003)

    Google Scholar 

  39. Yangui, R., Nabli, A., Gargouri, F.: Towards data warehouse schema design from social networks-dynamic discovery of multidimensional concepts. In: ICEIS, no. 1, pp. 338–345 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ladjel Bellatreche .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khouri, S., Berkani, N., Bellatreche, L., Lanasri, D. (2019). Data Cube Is Dead, Long Life to Data Cube in the Age of Web Data. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37188-3_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37187-6

  • Online ISBN: 978-3-030-37188-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics