Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
On the Advent of Operational Perspectives in Business Intelligence Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Deployment of a Descriptive Big Data Model

Authors : Marco Pospiech, Carsten Felden

Published in: Business Intelligence for New-Generation Managers

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Towards an Evaluation Framework to Structure Business Intelligence Project Patterns as Enhancement of Business Intelligence Maturity Models
next chapter Business Intelligence 2.0
Literature
1.
Pospiech M, Felden C (2012) Big data – a state-of-the-art. In: Proceedings of AMCIS 2012, pp 1–11
2.
3.
Bizer C, Boncz P, Brodie M (2011) The meaningful use of big data: four perspectives. SIGMOD 40(4):56–60CrossRef
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
5.
Simmhan Y, Barga R, Heasley J (2009) GrayWulf: scalable software architecture for data intensive computing. In: Proceedings of HICSS 2009, pp 1–10
6.
Miles M, Huberman A (1994) Qualitative data analysis. Sage, Thousand Oaks
7.
Flick U (2009) An introduction to qualitative research. Sage, London
8.
Glaser B, Strauss A (1967) The discovery of grounded theory. Aldine Transaction, Chicago
9.
Glaser B (1978) Theoretical sensitivity: advances in the methodology of grounded theory. Sociology Press, Mill Valley
10.
Cooper H (1998) Synthesizing research: a guide for literature reviews. Sage, Thousand Oaks
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
12.
Hughes J, Wood-Harper T (1999) Systems development as a research act. J Inf Technol 14(1):83–94CrossRef
13.
Strauss A, Corbin J (1990) Basics of qualitative research: grounded theory procedures and techniques. Sage, Thousand Oaks
14.
Gluchowski P, Gabriel R, Dittmar C (2008) Management support systeme und business intelligence. Springer, Berlin
15.
Hackathorn R (2012) Current practices in active data warehousing. DM review, white paper
16.
Chen H, Chiang R, Storey V (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165–1188
17.
Grant R (1996) Prospering in dynamically-competitive environments: organizational capability as knowledge integration. Organ Sci 7(4):375–387CrossRef
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
19.
Alavi M, Leidner D (2001) Review: knowledge management and knowledge management systems. MIS Q 25(1):107–136CrossRef
20.
Krumm J, Davies N, Narayanaswami C (2008) User-generated content. Pervasive Comput 7(4):10–11CrossRef
21.
22.
Bitincka L, Ganapathi A, Zhang S (2012) Experiences with workload management in Splunk. In: Proceedings of MBDS 2012, pp 25–30
23.
DiNucci D (1999) Fragmented future. Print 53:32–35
24.
Borkar V, Carey M, Li C (2012) Inside “big data management”. In: Proceedings of EDBT/ICDT 2012, pp 3–14
25.
Kaplan A, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of Social Media. Bus Horizons 53(1):59–68CrossRef
26.
27.
Sterling T, Stark D (2009) A high-performance computing forecast: partly cloudy. Comput Sci Eng 11(4):42–49CrossRef
28.
Freedman D, Kisilev P (2009) Fast mean shift by compact density representation. In: Proc CVPR recognition 2009, pp 1818–1825
29.
Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Proceedings of OSDI, pp 137–149
30.
Krcmar H (2012) Information management. Springer, Berlin
31.
Li X, Lillibridge M, Uysal M (2010) Reliability analysis of deduplicated and erasure-coded storage. Proc SIGMETRICS 38(3):4–9CrossRef
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
33.
Miner G, Delen D, Fast A, Eider J (2012) Practical text mining and statistical analysis for non-structured text data. Academic, Waltham
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
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
36.
Wasserman S, Faust K (1994) Social network analysis: methods and applications, structural analysis. Cambridge University Press, New YorkCrossRef
37.
Shmueli G, Koppius O (2011) Predictive analytics in information systems research. MIS Q 35(3):553–572
38.
Balsa Rodriguez M, Gobbetti E, Guitian M (2013) A survey of compressed GPU-based direct volume rendering. In: Proceedings of Eurographics 2013
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 77–100CrossRef
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
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):65–69CrossRef
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
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
44.
Toole J, Eagle N, Plotkin J (2011) Spatiotemporal correlations in criminal offense records. ACM Trans Intell Syst Technol 2(4), article 38
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
46.
Zhang Y, Gong B, Hui Liu Y (2011) Parallel option pricing with BSDEs method on MapReduce. In: Proceedings of ICCRD, pp 289–293
47.
Metadata
Title
Deployment of a Descriptive Big Data Model
Authors
Marco Pospiech
Carsten Felden
Copyright Year
2015
Book
Business Intelligence for New-Generation Managers

Print ISBN: 978-3-319-15695-8

Electronic ISBN: 978-3-319-15696-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-15696-5_7