Skip to main content

Information Requirements for Big Data Projects: A Review of State-of-the-Art Approaches

  • Conference paper
  • First Online:
Databases and Information Systems (DB&IS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 838))

Included in the following conference series:

Abstract

Big data technologies are rapidly gaining popularity and become widely used, thus, making the choice of developing methodologies including the approaches for requirements analysis more acute. There is a position that in the context of the Data Warehousing (DW), similar to other Decision Support Systems (DSS) technologies, defining information requirements (IR) can increase the chances of the project to be successful with its goals achieved. This way, it is important to examine this subject in the context of Big data due to the lack of research in the field of Big data requirements analysis. This paper gives an overview of the existing methods associated with Big data technologies and requirements analysis, and provides an evaluation by three types of criteria: (i) general characteristics, (ii) requirements analysis related, and (iii) Big data technologies related criteria. We summarize on the requirements analysis process in Big data projects, and explore solutions on how to (semi-) automate requirements engineering phases.

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

Access this chapter

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

References

  1. Beyer, M.A., Laney, D.: The importance of ‘big data’: a definition. Gartner, Stamford (2012)

    Google Scholar 

  2. Kart, L., Heudecker, N., Buytendijk, F.: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype. Gartner Inc. (2013)

    Google Scholar 

  3. Katal, A., Wazid, M., Goudar, R.H.: Big data: issues, challenges, tools and good practices. In: IC3 2013, pp. 404–409. IEEE Press (2013)

    Google Scholar 

  4. Tardio, R., Mate, A., Trujillo, J.: An iterative methodology for big data management, analysis and visualization. IEEE BigData 2015, pp. 545–550 (2015)

    Google Scholar 

  5. Di Tria, F., Lefons, E., Tangorra, F.: Design process for big data warehouses. In: DSAA 2014, pp. 512–518 (2014)

    Google Scholar 

  6. Kitchenham, B., Charters, S.: Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical report. Keele University (2007)

    Google Scholar 

  7. Sinaeepourfard, A., Garcia, J., Masip-Bruin, X., et al.: Towards a comprehensive data lifecycle model for big data environments. In: BDCAT 2016, pp. 100–106. ACM, New York (2016)

    Google Scholar 

  8. Caldarola, E.G., Picariello, A., Castelluccia, D.: Modern enterprises in the bubble: why big data matters. SIGSOFT Softw. Eng. Notes 40(1), 1–4 (2015)

    Article  Google Scholar 

  9. Santos, M.Y., Costa, C.: Data warehousing in big data: from multidimensional to tabular data models. In: C3S2E 2016, pp. 51–60. ACM, New York (2016)

    Google Scholar 

  10. Arruda, D., Madhavji, N.H.: Towards a requirements engineering artefact model in the context of big data software development projects: Research in progress. IEEE Big Data 2017, pp. 2314–2319 (2017)

    Google Scholar 

  11. Eridaputra, H., Hendradjaya, B., Sunindyo, W.D.: Modeling the requirements for big data application using goal oriented approach. In: ICODSE’14 (2015)

    Google Scholar 

  12. Ardagna, C.A., Ceravolo, P., Cota, et al.: What are my users looking for when preparing a big data campaign. IEEE BigData Congress 2017, pp. 201–208 (2017)

    Google Scholar 

  13. Abdullah, T., Ahmet, A.: Genomics analyser: a big data framework for analysing genomics data. In: BDCAT 2017, pp. 189–197. ACM, New York (2017)

    Google Scholar 

  14. Liu, J., Shang, J., Wang, C., et al.: Mining quality phrases from massive text corpora. In: SIGMOD 2015, pp. 1729–1744 (2015)

    Google Scholar 

  15. Cheptsov, A., Tenschert, A., Schmidt, P., Glimm, B., Matthesius, M., Liebig, T.: Introducing a new scalable data-as-a-service cloud platform for enriching traditional text mining techniques by integrating ontology modelling and natural language processing. In: Huang, Z., Liu, C., He, J., Huang, G. (eds.) WISE 2013. LNCS, vol. 8182, pp. 62–74. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54370-8_6

    Chapter  Google Scholar 

  16. Mallek, H., Ghozzi, F., Teste, O., Gargouri, F.: BigDimETL: ETL for multidimensional big data. In: Madureira, A.M., Abraham, A., Gamboa, D., Novais, P. (eds.) ISDA 2016. AISC, vol. 557, pp. 935–944. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53480-0_92

    Chapter  Google Scholar 

  17. Tikito, I., Souissi, N.: Data collect requirements model. In: BDCA 2017, 7 p. ACM, New York (2017). Article 4

    Google Scholar 

  18. Di Francescomarino, C., et al.: Semantic-based process analysis. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 228–243. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11915-1_15

    Chapter  Google Scholar 

  19. Madhavji, N.H., Miranskyy, A., Kontogiannis, K.: Big picture of big data software engineering: with example research challenges. In: BIGDSE 2015, pp. 11–14. IEEE Press (2015)

    Google Scholar 

  20. Shao, G., Shin, S., Jain, S.: Data analytics using simulation for smart manufacturing. In: Proceedings of the Winter Simulation Conference, pp. 2192–2203. IEEE Press (2014)

    Google Scholar 

  21. Fiore, S., et al.: Big data analytics on large-scale scientific datasets in the INDIGO-datacloud project. In: CF 2015, pp. 343–348. ACM, New York (2017)

    Google Scholar 

  22. Yasin, A., Liu, L., Cao, Z., Wang, J., Liu, Y., Ling, T.S.: Big data services requirements analysis. In: Kamalrudin, M., Ahmad, S., Ikram, N. (eds.) APRES 2017. CCIS, vol. 809, pp. 3–14. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7796-8_1

    Chapter  Google Scholar 

  23. Fernandez-Garcia, A.J., Iribarne, L., Corral, A., Wang, James Z.: Evolving mashup interfaces using a distributed machine learning and model transformation methodology. In: Ciuciu, I., et al. (eds.) OTM 2015. LNCS, vol. 9416, pp. 401–410. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26138-6_43

    Chapter  Google Scholar 

  24. Ji, J., Peng, R.: An analysis pattern driven requirements modeling method. In: REW Workshops, IEEE International, pp. 316–319. IEEE Press (2016)

    Google Scholar 

  25. Santos, J.C., et al.: BUDGET: a tool for supporting software architecture traceability research. In: WICSA 2016, pp. 303–306. IEEE Press (2016)

    Google Scholar 

  26. Nesi, P., Pantaleo, G., Sanesi, G.: A hadoop based platform for natural language processing of web pages and documents. J. Vis. Lang. Comput. 31, 130–138 (2015)

    Article  Google Scholar 

  27. Zhang, Y., Chen, Y., Ma, Y.: A framework for data-driven automata design. In: Liu, L., Aoyama, M. (eds.) Requirements Engineering in the Big Data Era. CCIS, vol. 558, pp. 33–47. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48634-4_3

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Natalija Kozmina .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kozmina, N., Niedrite, L., Zemnickis, J. (2018). Information Requirements for Big Data Projects: A Review of State-of-the-Art Approaches. In: Lupeikiene, A., Vasilecas, O., Dzemyda, G. (eds) Databases and Information Systems. DB&IS 2018. Communications in Computer and Information Science, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-319-97571-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97571-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97570-2

  • Online ISBN: 978-3-319-97571-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics