How Big Does Big Data Need to Be?

Publikation: Beiträge in SammelwerkenAufsätze in SammelwerkenForschungbegutachtet

Standard

How Big Does Big Data Need to Be? / Stange, Martin; Funk, Burkhardt.
Enterprise Big Data Engineering, Analytics, and Management. Hrsg. / Martin Atzmueller; Samia Oussena; Thomas Roth-Berghofer. Hershey: Business Science Reference, 2016. S. 1-12.

Publikation: Beiträge in SammelwerkenAufsätze in SammelwerkenForschungbegutachtet

Harvard

Stange, M & Funk, B 2016, How Big Does Big Data Need to Be? in M Atzmueller, S Oussena & T Roth-Berghofer (Hrsg.), Enterprise Big Data Engineering, Analytics, and Management. Business Science Reference, Hershey, S. 1-12. https://doi.org/10.4018/978-1-5225-0293-7.ch001

APA

Stange, M., & Funk, B. (2016). How Big Does Big Data Need to Be? In M. Atzmueller, S. Oussena, & T. Roth-Berghofer (Hrsg.), Enterprise Big Data Engineering, Analytics, and Management (S. 1-12). Business Science Reference. https://doi.org/10.4018/978-1-5225-0293-7.ch001

Vancouver

Stange M, Funk B. How Big Does Big Data Need to Be? in Atzmueller M, Oussena S, Roth-Berghofer T, Hrsg., Enterprise Big Data Engineering, Analytics, and Management. Hershey: Business Science Reference. 2016. S. 1-12 doi: 10.4018/978-1-5225-0293-7.ch001

Bibtex

@inbook{f6911d81026546388fc927733a57d709,
title = "How Big Does Big Data Need to Be?",
abstract = "Collecting and storing of as many data as possible is common practice in many companies these days. To reduce costs of collecting and storing data that is not relevant, it is important to define which analytical questions are to be answered and how much data is needed to answer these questions. In this chapter,a process to define an optimal sampling size is proposed. Based on benefit/cost considerations, the authors show how to find the sample size that maximizes the utility of predictive analytics. By applying the proposed process to a case study is shown that only a very small fraction of the available data set is needed to make accurate predictions.",
keywords = "Business informatics, Big Data, Predictive Analytics, Learning Curve",
author = "Martin Stange and Burkhardt Funk",
year = "2016",
month = jun,
doi = "10.4018/978-1-5225-0293-7.ch001",
language = "English",
isbn = "9781522502937",
pages = "1--12",
editor = "Martin Atzmueller and Samia Oussena and Thomas Roth-Berghofer",
booktitle = "Enterprise Big Data Engineering, Analytics, and Management",
publisher = "Business Science Reference",
address = "United States",

}

RIS

TY - CHAP

T1 - How Big Does Big Data Need to Be?

AU - Stange, Martin

AU - Funk, Burkhardt

PY - 2016/6

Y1 - 2016/6

N2 - Collecting and storing of as many data as possible is common practice in many companies these days. To reduce costs of collecting and storing data that is not relevant, it is important to define which analytical questions are to be answered and how much data is needed to answer these questions. In this chapter,a process to define an optimal sampling size is proposed. Based on benefit/cost considerations, the authors show how to find the sample size that maximizes the utility of predictive analytics. By applying the proposed process to a case study is shown that only a very small fraction of the available data set is needed to make accurate predictions.

AB - Collecting and storing of as many data as possible is common practice in many companies these days. To reduce costs of collecting and storing data that is not relevant, it is important to define which analytical questions are to be answered and how much data is needed to answer these questions. In this chapter,a process to define an optimal sampling size is proposed. Based on benefit/cost considerations, the authors show how to find the sample size that maximizes the utility of predictive analytics. By applying the proposed process to a case study is shown that only a very small fraction of the available data set is needed to make accurate predictions.

KW - Business informatics

KW - Big Data

KW - Predictive Analytics

KW - Learning Curve

UR - http://www.igi-global.com/chapter/how-big-does-big-data-need-to-be/154550

U2 - 10.4018/978-1-5225-0293-7.ch001

DO - 10.4018/978-1-5225-0293-7.ch001

M3 - Contributions to collected editions/anthologies

SN - 9781522502937

SP - 1

EP - 12

BT - Enterprise Big Data Engineering, Analytics, and Management

A2 - Atzmueller, Martin

A2 - Oussena, Samia

A2 - Roth-Berghofer, Thomas

PB - Business Science Reference

CY - Hershey

ER -

DOI

Zuletzt angesehen

Publikationen

  1. Do We Really Know The Benefit Of Machine Learning In Production Planning And Control? A Systematic Review Of Industry Case Studies
  2. Is globalization healthy
  3. Compression behaviour of wire + arc additive manufactured structures
  4. Competition response of European beech Fagus sylvatica L. varies with tree size and abiotic stress
  5. Questions liées au genre dans la scène berlinoise de l’electronic dance music
  6. A fragile kaleidoscope
  7. Towards a Deconstruction of the Screen
  8. Inadequate Assessment of the Ecosystem Service Rationale for Conservation
  9. Understanding role models for change
  10. Schreiben
  11. The influence of Reputation on Travel Decisions in the Internet
  12. Personality in personnel selection and assessment
  13. Collaborative innovation online
  14. Mapping the determinants of carbon-related CEO compensation
  15. Coming to work while sick
  16. Stadtentwicklung und Migration
  17. Building a digital anchor
  18. Green Big Data – eine Green IT/Green IS Perspektive auf Big Data
  19. Applying the energy cultures framework to understand energy systems in the context of rural sustainability transformation
  20. Bodenlos.
  21. The role of scenarios in fostering collective action for sustainable development
  22. As You Like It
  23. Ballons
  24. NGOs
  25. Drawing Lessons: Ruth Asawa’s Early Work on Paper
  26. How Participatory Should Environmental Governance Be?
  27. Tree species richness strengthens relationships between ants and the functional composition of spider assemblages in a highly diverse forest
  28. Mitteilung zur Kopula von Aeshna viridis
  29. Playing the past to understand the present
  30. SAMT
  31. Working in the "Global Village"
  32. Who participates in which type of teacher professional development?
  33. Mysthik
  34. Art and Culture as an Urban Development Tool