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Deployment of a Descriptive Big Data Model

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Business Intelligence for New-Generation Managers

Abstract

Big Data is an emerging research topic. The term remains fuzzy and jeopardizes to become an umbrella term. Straight forward investigations are inhibited since the research field is not well defined, yet. To identify a common understanding, experts have been interviewed. Hereby, the findings are coded and conceptualized until a descriptive Big Data model is developed by using Grounded Theory. This provides the basis for the model’s deployment. Here, academic publications and practical implementations marked as Big Data are classified. It becomes evident that Big Data is use-case driven and forms an interdisciplinary research field. Even not all papers belong to this research field. The findings become confirmed by the practical implementations. The chapter contributes to the intensive discussion about the term Big Data in illustrating the underlying area of discourse. A classification to set the research area apart from others can be achieved to support a goal oriented research in future.

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References

  1. Pospiech M, Felden C (2012) Big data – a state-of-the-art. In: Proceedings of AMCIS 2012, pp 1–11

    Google Scholar 

  2. Gartner Inc (2013) IT glossary big data. http://www.gartner.com/it-glossary/big-data. Accessed 28 June 2013

  3. Bizer C, Boncz P, Brodie M (2011) The meaningful use of big data: four perspectives. SIGMOD 40(4):56–60

    Article  Google Scholar 

  4. He Y, Lee R, Huai Y (2011) RCFile: a fast and space-efficient data placement structure in MapReduce-based warehouse systems. In: Proceedings of ICDE 2011, pp 1199–1208

    Google Scholar 

  5. Simmhan Y, Barga R, Heasley J (2009) GrayWulf: scalable software architecture for data intensive computing. In: Proceedings of HICSS 2009, pp 1–10

    Google Scholar 

  6. Miles M, Huberman A (1994) Qualitative data analysis. Sage, Thousand Oaks

    Google Scholar 

  7. Flick U (2009) An introduction to qualitative research. Sage, London

    Google Scholar 

  8. Glaser B, Strauss A (1967) The discovery of grounded theory. Aldine Transaction, Chicago

    Google Scholar 

  9. Glaser B (1978) Theoretical sensitivity: advances in the methodology of grounded theory. Sociology Press, Mill Valley

    Google Scholar 

  10. Cooper H (1998) Synthesizing research: a guide for literature reviews. Sage, Thousand Oaks

    Google Scholar 

  11. Hughes J, Jones S (2003) Reflections on the use of grounded theory in interpretive information systems research. In: Proceedings of ECIS 2003, paper 62

    Google Scholar 

  12. Hughes J, Wood-Harper T (1999) Systems development as a research act. J Inf Technol 14(1):83–94

    Article  Google Scholar 

  13. Strauss A, Corbin J (1990) Basics of qualitative research: grounded theory procedures and techniques. Sage, Thousand Oaks

    Google Scholar 

  14. Gluchowski P, Gabriel R, Dittmar C (2008) Management support systeme und business intelligence. Springer, Berlin

    Google Scholar 

  15. Hackathorn R (2012) Current practices in active data warehousing. DM review, white paper

    Google Scholar 

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

    Google Scholar 

  17. Grant R (1996) Prospering in dynamically-competitive environments: organizational capability as knowledge integration. Organ Sci 7(4):375–387

    Article  Google Scholar 

  18. Barney J, Wright M, David J, Ketchen J (2001) The resource-based view of the firm: ten years after 1991. J Manag 27(6):625–641

    Google Scholar 

  19. Alavi M, Leidner D (2001) Review: knowledge management and knowledge management systems. MIS Q 25(1):107–136

    Article  Google Scholar 

  20. Krumm J, Davies N, Narayanaswami C (2008) User-generated content. Pervasive Comput 7(4):10–11

    Article  Google Scholar 

  21. Monash C (2010) Three broad categories of data. http://www.dbms2.com/2010/01/17/three-broad-categories-of-data. Accessed 8 July 2013

  22. Bitincka L, Ganapathi A, Zhang S (2012) Experiences with workload management in Splunk. In: Proceedings of MBDS 2012, pp 25–30

    Google Scholar 

  23. DiNucci D (1999) Fragmented future. Print 53:32–35

    Google Scholar 

  24. Borkar V, Carey M, Li C (2012) Inside “big data management”. In: Proceedings of EDBT/ICDT 2012, pp 3–14

    Google Scholar 

  25. Kaplan A, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of Social Media. Bus Horizons 53(1):59–68

    Article  Google Scholar 

  26. IBM Coop (2013) What is big data. http://www-01.ibm.com/software/data/bigdata. Accessed 10 July 2013

  27. Sterling T, Stark D (2009) A high-performance computing forecast: partly cloudy. Comput Sci Eng 11(4):42–49

    Article  Google Scholar 

  28. Freedman D, Kisilev P (2009) Fast mean shift by compact density representation. In: Proc CVPR recognition 2009, pp 1818–1825

    Google Scholar 

  29. Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Proceedings of OSDI, pp 137–149

    Google Scholar 

  30. Krcmar H (2012) Information management. Springer, Berlin

    Google Scholar 

  31. Li X, Lillibridge M, Uysal M (2010) Reliability analysis of deduplicated and erasure-coded storage. Proc SIGMETRICS 38(3):4–9

    Article  Google Scholar 

  32. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: an overview. In: Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, pp 37–54

    Google Scholar 

  33. Miner G, Delen D, Fast A, Eider J (2012) Practical text mining and statistical analysis for non-structured text data. Academic, Waltham

    Google Scholar 

  34. Wagner L, Van Belle J (2007) Web mining for strategic intelligence: South African experiences and a practical methodology. In: Proceedings of ICDSS 2007, paper 1

    Google Scholar 

  35. Lukashevich H, Nowak S, Dunker P (2009) Using one-class SVM outliers detection for verification of collaboratively tagged image training sets. In: Proceedings of ICME 2009, pp 682–685

    Google Scholar 

  36. Wasserman S, Faust K (1994) Social network analysis: methods and applications, structural analysis. Cambridge University Press, New York

    Book  Google Scholar 

  37. Shmueli G, Koppius O (2011) Predictive analytics in information systems research. MIS Q 35(3):553–572

    Google Scholar 

  38. Balsa Rodriguez M, Gobbetti E, Guitian M (2013) A survey of compressed GPU-based direct volume rendering. In: Proceedings of Eurographics 2013

    Google Scholar 

  39. Hartmann S (1996) The world as a process: simulations in the natural and social sciences. In: Hegselmann R, Mueller U, Troitzsch K (eds) Modeling and simulation in the social sciences from the philosophy of science point of view. Kluwer Academic, Dordrecht, pp 77–100

    Chapter  Google Scholar 

  40. Chailan R, Bouchette F, Dumontier C (2012) High performance pre-computing: prototype application to a coastal flooding decision tool. Knowledge and systems engineering (KSE). In: Proceedings of KSE 2012, pp 195–202

    Google Scholar 

  41. Buhl H, Röglinger M, Moser F, Heidemann J (2013) Big data – a fashionable topic with(out) sustainable relevance for research and practice? Bus Inf Syst Eng 5(2):65–69

    Article  Google Scholar 

  42. Kohlwey E, Sussman A, Trost J (2011) Leveraging the cloud for big data biometrics. In: Proceedings of world congress services 2011, pp 597–601

    Google Scholar 

  43. Zimbra D, Chen H (2011) Stakeholder approach to stock prediction using finance social media. In: Chen H (ed) Intelligent systems smart market and money. IEEE, Washington, DC, pp 88–92

    Google Scholar 

  44. Toole J, Eagle N, Plotkin J (2011) Spatiotemporal correlations in criminal offense records. ACM Trans Intell Syst Technol 2(4), article 38

    Google Scholar 

  45. Venkataraman S, Tolia N, Ranganathan P (2011) Consistent and durable data structures for non-volatile byte- addressable memory. In: Proceedings of USENIX 2011, pp 1–15

    Google Scholar 

  46. Zhang Y, Gong B, Hui Liu Y (2011) Parallel option pricing with BSDEs method on MapReduce. In: Proceedings of ICCRD, pp 289–293

    Google Scholar 

  47. TechAmerica, Foundation Big Data Commission (2013) http://www.techamericafoundation.org/bigdata. Accessed 28 Oct 2013

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Author information

Authors and Affiliations

  1. Technische Universität Bergakademie, Freiberg, Germany

    Marco Pospiech & Carsten Felden

Authors
  1. Marco Pospiech
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  2. Carsten Felden
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Corresponding author

Correspondence to Marco Pospiech .

Editor information

Editors and Affiliations

  1. School of Management, University of St. Gallen, St. Gallen, Switzerland

    Jörg H. Mayer

  2. Chair of Accounting and Auditing, Darmstadt University of Technology, Darmstadt, Germany

    Reiner Quick

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Pospiech, M., Felden, C. (2015). Deployment of a Descriptive Big Data Model. In: Mayer, J., Quick, R. (eds) Business Intelligence for New-Generation Managers. Springer, Cham. https://doi.org/10.1007/978-3-319-15696-5_7

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