Skip to main content
SpringerLink
Log in

Big Data

  • Living reference work entry
  • First Online:
Handbuch Digitale Wirtschaft

Part of the book series: Springer Reference Wirtschaft ((SRW))

Zusammenfassung

Der vorliegende Beitrag gibt eine grundlegende Einführung zu dem Begriff Big Data. Nach einer kurzen Darstellung der Relevanz und Aktualität des Themas, wird im Folgenden auf den Begriff selbst, und die ihm zugrunde liegenden Charakteristiken der Daten eingegangen. Im Anschluss erfolgt eine Vorstellung technischer Grundlagen, wobei ausgewählte Konzepte dediziert behandelt werden. Zur Veranschaulichung werden anschließend einige typische Einsatzgebiete, sowie konkrete Anwendungsfälle beschrieben. Abschließend folgen eine Betrachtung der Herausforderungen bei der Durchführung von Big Data Projekten, sowie ein Ausblick auf die zu erwartenden zukünftigen Entwicklungen und gesellschaftlichen Implikationen.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    www.scopus.com.

  2. 2.

    Ein Zettabyte entspricht der Menge von einer Trillion Gigabyte. Würde man die Gesamtmenge von 175 ZB auf DVD Rohlingen speichern, wäre man in der Lage, 23 Stapel zu bilden, die jeweils von der Erde bis zum Mond reichen würden (Reinsel et al. 2018).

  3. 3.

    Der erste Eintrag Hadoops ist im Repository der Apache Foundation (www.apache.org) auf den 01.04.2006 datiert (https://archive.apache.org/dist/hadoop/core/).

Literatur

  • Alaei, Ali Reza, Susanne Becken, und Bela Stantic. 2019. Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research 58(2): 175–191. https://doi.org/10.1177/0047287517747753.

    Article  Google Scholar 

  • Azimi, Iman, Arman Anzanpour, Amir M. Rahmani, Pasi Liljeberg, und Tapio Salakoski. 2016. Medical warning system based on Internet of Things using fog computing. In 2016 international workshop on big data and information security, 19–24. Indonesia/Piscataway: IEEE.

    Google Scholar 

  • Bedi, Punam, Vinita Jindal, und Anjali Gautam. 2014. Beginning with big data simplified. In International conference on data mining and intelligent computing, 1–7. Delhi, 05.09.2014–06.09.2014.

    Google Scholar 

  • BITKOM. 2012. Big Data im Praxiseinsatz – Szenarien, Beispiele, Effekte. Hrsg. v. BITKOM. Berlin. https://www.bitkom.org/sites/default/files/pdf/noindex/Publikationen/2012/Leitfaden/Leitfaden-Big-Data-im-Praxiseinsatz-Szenarien-Beispiele-Effekte/BITKOM-LF-big-data-2012-online1.pdf. Zugegriffen am 05.03.2020.

  • BITKOM. 2014. Big-Data-Technologien–Wissen für Entscheider. Hrsg. v. BITKOM. Berlin. https://www.bitkom.org/sites/default/files/file/import/140228-Big-Data-Technologien-Wissen-fuer-Entscheider.pdf. Zugegriffen am 05.03.2020.

  • Bonesso, Sara, Elena Bruni, und Fabrizio Gerli. 2020. The organizational challenges of big data. In Behavioral competencies of digital professionals. Understanding the role of emotional intelligence, Bd. 48. 1st ed. 2020, Hrsg. Sara Bonesso, Elena Bruni und Fabrizio Gerli, 1–19. Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Brady, Henry E. 2019. The challenge of big data and data science. Annual Review of Political Science 22(1): 297–323. https://doi.org/10.1146/annurev-polisci-090216-023229.

    Article  Google Scholar 

  • Bronson, Kelly, und Irena Knezevic. 2016. Big data in food and agriculture. Big Data & Society 3(1). https://doi.org/10.1177/2053951716648174.

  • Chang, Fay, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, et al. 2006. Bigtable: A distributed storage system for structured data. In 7th USENIX symposium on Operating Systems Design and Implementation (OSDI), 205–218. Berkeley: USENIX Association.

    Google Scholar 

  • Chang, Wo. L., und Nancy Grady. 2019. NIST big data interoperability framework: Volume 1, Definitions, Version 3. https://doi.org/10.6028/NIST.SP.1500-1r2.

  • Chen, Min, Shiwen Mao, und Yunhao Liu. 2014. Big data: A survey. Mobile Netw Appl 19(2): 171–209. https://doi.org/10.1007/s11036-013-0489-0.

    Article  Google Scholar 

  • Da Xu, Li, und Lian Duan. 2019. Big data for cyber physical systems in industry 4.0: A survey. Enterprise Information Systems 13(2): 148–169. https://doi.org/10.1080/17517575.2018.1442934.

    Article  Google Scholar 

  • Davenport, Thomas. 2014. Big data at work. Dispelling the myths, uncovering the opportunities. Boston: Harvard Business Review Press.

    Google Scholar 

  • Dean, Jeffrey, und Sanjay Ghemawat. 2004. MapReduce: Simplified data processing on large clusters. New York: Association for Computing Machinery.

    Google Scholar 

  • Demchenko, Yuri, Paola Grosso, Cees de Laat, und Peter Membrey. 2013. Addressing big data issues in scientific data infrastructure. In International conference on collaboration technologies and systems, 48–55. San Diego, 20.05.2013–24.05.2013.

    Google Scholar 

  • Dijcks, JP. 2013. Oracle: Big data for the enterprise. Hrsg. v. Oracle Corporation. Redwood. http://www.oracle.com/us/products/database/big-data-for-enterprise-519135.pdf. Zugegriffen am 05.03.2020.

  • Domdouzis, Konstantinos, Babak Akhgar, Simon Andrews, Helen Gibson, und Laurence Hirsch. 2016. A social media and crowdsourcing data mining system for crime prevention during and post-crisis situations. Journal of Systems and Information Technology 18(4): 364–382. https://doi.org/10.1108/JSIT-06-2016-0039.

    Article  Google Scholar 

  • Filipiak, Dominik, Milena Stróżyna, Krzysztof Węcel, und Witold Abramowicz. 2018. Big data for anomaly detection in maritime surveillance: Spatial AIS data analysis for Tankers. Zeszyty Naukowe Akademii Marynarki Wojennej 215(4): 5–28. https://doi.org/10.2478/sjpna-2018-0024.

    Article  Google Scholar 

  • Fiore, Sandro, Donatello Elia, Carlos Eduardo Pires, Demetrio Gomes Mestre, Cinzia Cappiello, Monica Vitali, et al. 2019. An integrated big and fast data analytics platform for smart urban transportation management. IEEE Access 7: 117652–117677. https://doi.org/10.1109/ACCESS.2019.2936941.

    Article  Google Scholar 

  • Gandomi, Amir, und Murtaza Haider. 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management 35(2): 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007.

    Article  Google Scholar 

  • Ghemawat, Sanjay, Howard Gobioff, und Shun-Tak Leung. 2003. The Google file system. In Proceedings of the nineteenth ACM symposium on Operating systems principles, Hrsg. Michael L. Scott. Bolton Landing: ACM.

    Google Scholar 

  • Günther, Wendy Arianne, Rezazade Mehrizi, Mohammad Hosein, Marleen Huysman, und Frans Feldberg. 2017. Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems 26(3): 191–209. https://doi.org/10.1016/j.jsis.2017.07.003.

    Article  Google Scholar 

  • Hassan, Ahmad Pajam. 2019. Enhancing supply chain risk management by applying machine learning to identify risks. In Business information systems 354. Lecture Notes in Business Information Processing, Hrsg. Witold Abramowicz und Rafael Corchuelo, 191–205. Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Hu, Yuheng, David Gal, und Yili Hong. 2018. Modeling brand personality with business value of social media analytics: Predicting brand personality with user-generated content and firm-generated content. In Proceedings of the 39th ICIS. San Francisco: International Conference on Information Systems, 13.12.2018–16.12.2018.

    Google Scholar 

  • IBM. 2013. Analytics: The real-world use of big data. https://www.ibmbigdatahub.com/whitepaper/analytics-real-world-use-big-data. Zugegriffen am 12.02.2020.

  • International Data Corporation. 2019. IDC forecasts revenues for big data and business analytics solutions will reach $189.1 billion this year with double-digit annual growth through 2022. https://www.idc.com/getdoc.jsp?containerId=prUS44998419, zuletzt aktualisiert am 04.04.2019. Zugegriffen am 09.03.2020.

  • Izadi, Davood, Jemal H. Abawajy, Sara Ghanavati, und Tutut Herawan. 2015. A data fusion method in wireless sensor networks. Sensors (Basel, Switzerland) 15(2): 2964–2979. https://doi.org/10.3390/s150202964.

    Article  Google Scholar 

  • Khazaei, Hamzeh, Saeed Zareian, Rodrigo Veleda, und Marin Litoiu. 2016. Sipresk: A big data analytic platform for smart transportation. In Smart city 360°. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 166, Hrsg. Alberto Leon-Garcia, 419–430. Cham: Springer.

    Chapter  Google Scholar 

  • Kollmann, Tobias, und Holger Schmidt. 2016. Technologie 4.0. In Deutschland 4.0, Hrsg. Tobias Kollmann und Holger Schmidt, 43–53. Wiesbaden: Springer Fachmedien.

    Chapter  Google Scholar 

  • Kreps J. 2014. Questioning the Lambda architecture. The Lambda architecture has its merits, but alternatives are worth exploring. O’Reilly Media. https://www.oreilly.com/ideas/questioning-the-lambda-architecture. Zugegriffen am 21.01.2020.

  • Landrock, Holm, und Andreas Gadatsch. 2018. Anwendungsszenarien für Big Data im Gesundheitswesen. In Big Data im Gesundheitswesen kompakt. Konzepte, Lösungen, Visionen, Bd. 2014, Hrsg. Holm Landrock und Andreas Gadatsch, 39–61. Wiesbaden: Springer Vieweg. (IT kompakt).

    Chapter  Google Scholar 

  • Laney, Doug. 2001. 3D data management: Controlling data volume, velocity and variety. META group research note 6(70): 1.

    Google Scholar 

  • LinkedIn. 2018. LinkedIn workforce report|United States|August 2018. https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018, zuletzt aktualisiert am 10.08.2018. Zugegriffen am 09.03.2020.

  • Liu, Jianzheng, Jie Li, Weifeng Li, und Jiansheng Wu. 2016. Rethinking big data: A review on the data quality and usage issues. ISPRS Journal of Photogrammetry and Remote Sensing 115:134–142. https://doi.org/10.1016/j.isprsjprs.2015.11.006.

    Article  Google Scholar 

  • Lunde, Trygve Åse, Atilla Paul Sjusdal, und Ilias O. Pappas. 2019. Organizational culture challenges of adopting big data: A systematic literature Review. In Digital transformation for a sustainable society in the 21st century. Lecture Notes in Computer Science, Hrsg. Ilias O. Pappas, Patrick Mikalef, Yogesh K. Dwivedi, Letizia Jaccheri, John Krogstie und Matti Mäntymäki, Bd. 11701, 164–176. Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Mainzer, Klaus. 2019. Künstliche Intelligenz und Verantwortung. In Künstliche Intelligenz – Wann übernehmen die Maschinen? Hrsg. Klaus Mainzer, 267–279. Berlin/Heidelberg: Springer Berlin Heidelberg (Technik im Fokus).

    Chapter  Google Scholar 

  • Marz, Nathan, und James Warren. 2015. Big data. Principles and best practices of scalable real-time data systems. Shelter Island: Manning.

    Google Scholar 

  • Mayer-Schönberger, Viktor, und Kenneth Cukier. 2013. Big data. A revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.

    Google Scholar 

  • McNulty, Eileen. 2014. Peter Thiel: Big data is nothing but a buzzword. https://dataconomy.com/2014/10/peter-thiel-big-data-is-nothing-but-a-buzzword/, zuletzt aktualisiert am 09.10.2014. Zugegriffen am 13.03.2020.

  • Mourtzis, D., E. Vlachou, und N. Milas. 2016. Industrial big data as a result of IoT adoption in manufacturing. Procedia CIRP 55:290–295. https://doi.org/10.1016/j.procir.2016.07.038.

    Article  Google Scholar 

  • Mrozek, Dariusz. 2018. Foundations of the hadoop ecosystem. In Scalable big data analytics for protein bioinformatics: Efficient computational solutions for protein structures, 137–150. Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Müller, Oliver, Maria Fay, und Jan Vom Brocke. 2018. The effect of big data and analytics on firm performance: An econometric analysis considering industry Characteristics. Journal of Management Information Systems 35(2): 488–509. https://doi.org/10.1080/07421222.2018.1451955.

    Article  Google Scholar 

  • Nadal, Sergi, Victor Herrero, Oscar Romero, Alberto Abelló, Xavier Franch, Stijn Vansummeren, und Danilo Valerio. 2017. A software reference architecture for semantic-aware big data systems. Information and Software Technology 90:75–92. https://doi.org/10.1016/j.infsof.2017.06.001.

    Article  Google Scholar 

  • Nagorny, Kevin, Pedro Lima-Monteiro, Jose Barata, und Armando Walter Colombo. 2017. Big data analysis in smart manufacturing: A review. IJCNS 10(03): 31–58. https://doi.org/10.4236/ijcns.2017.103003.

    Article  Google Scholar 

  • Parlina, Anne, Kalamullah Ramli, und Hendri Murfi. 2020. Theme mapping and bibliometrics analysis of one decade of big data research in the scopus database. Information 11(2):69–94. https://doi.org/10.3390/info11020069.

    Article  Google Scholar 

  • Reinsel, David, John Gantz, und John Rydning. 2018. The digitization of the world – from edge to core. IDC. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf. Zugegriffen am 05.03.2020.

  • Rekha, J. H., und R. Parvathi. 2015. Survey on software project risks and big data analytics. Procedia Computer Science 50: 295–300. https://doi.org/10.1016/j.procs.2015.04.045.

    Article  Google Scholar 

  • Richards, Neil M., und Jonathan H. King. 2014. Big data ethics. Wake Forest Law Review 49(1): 393–432.

    Google Scholar 

  • Schofield, Jack. 2013. Gartner’s 2013 emerging technologies hype cycle focuses on humans and machines|ZDNet. https://www.zdnet.com/article/gartners-2013-emerging-technologies-hype-cycle-focuses-on-humans-and-machines/, zuletzt aktualisiert am 19.08.2013. Zugegriffen am 13.03.2020.

  • Staegemann, Daniel, Matthias Volk, Naoum Jamous, und Klaus Turowski. 2019. Understanding issues in big data applications – A multidimensional endeavor. In Twenty-fifth Americas conference on information systems. Cancun.

    Google Scholar 

  • Ta-Shma, Paula, Adnan Akbar, Guy Gerson-Golan, Guy Hadash, Francois Carrez, und Klaus Moessner. 2018. An ingestion and analytics architecture for IoT applied to smart city use cases. IEEE Internet of Things Journal 5(2): 765–774. https://doi.org/10.1109/JIOT.2017.2722378.

    Article  Google Scholar 

  • TNS Infratest. 2016. Digitale Begriffe für Bundesbürger noch immer Neuland. https://www.tns-infratest.com/presse/presseinformation.asp?prID=3474, zuletzt aktualisiert am 25.02.2016. Zugegriffen am 04.03.2020.

  • Vogel, Oliver, Ingo Arnold, Arif Chughtai, Edmund Ihler, Timo Kehrer, Uwe Mehlig, und Uwe Zdun. 2009. Software-Architektur. Heidelberg: Spektrum Akademischer.

    Book  Google Scholar 

  • Volk, Matthias, Stefan Willi Hart, Sascha Bosse, und Klaus Turowski. 2016. How much is big data? A classification framework for IT projects and technologies Diego, CA, USA, August 11–14, 2016. In 22nd Americas Conference on Information Systems, AMCIS 2016. Iraklion: AIS.

    Google Scholar 

  • Volk, Matthias, Daniel Staegemann, Matthias Pohl, und Klaus Turowski. 2019. Challenging big data engineering: Positioning of current and future development. In Proceedings of the 4th international conference on internet of things, big data and security, 351–358. Heraklion, 02.05.2019–04.05.2019: SCITEPRESS – Science and Technology Publications.

    Chapter  Google Scholar 

  • Ward, Jonathan Stuart, und Adam Barker. 2013. Undefined by data: a survey of big data definitions. In: arXiv preprint arXiv:1309.5821.

    Google Scholar 

  • Wu, Desheng, und Yiwen Cui. 2018. Disaster early warning and damage assessment analysis using social media data and geo-location information. Decision Support Systems 111:48–59. https://doi.org/10.1016/j.dss.2018.04.005.

    Article  Google Scholar 

  • Ylijoki, Ossi, und Jari Porras. 2016. Perspectives to definition of big data: A mapping study and discussion. jim 4(1): 69–91. https://doi.org/10.24840/2183-0606_004.001_0006.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Magdeburg Research and Competence Cluster for Very Large Business Applications (MRCC VLBA), Otto-von-Guericke-Universität Magdeburg, Magdeburg, Deutschland

    Matthias Volk, Daniel Staegemann & Klaus Turowski

Authors
  1. Matthias Volk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Staegemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Klaus Turowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Matthias Volk , Daniel Staegemann or Klaus Turowski .

Editor information

Editors and Affiliations

  1. Universität Duisburg-Essen, Essen, Germany

    Tobias Kollmann

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Volk, M., Staegemann, D., Turowski, K. (2020). Big Data. In: Kollmann, T. (eds) Handbuch Digitale Wirtschaft. Springer Reference Wirtschaft . Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-17345-6_71-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-17345-6_71-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer Gabler, Wiesbaden

  • Print ISBN: 978-3-658-17345-6

  • Online ISBN: 978-3-658-17345-6

  • eBook Packages: Springer Referenz Wirtschaftswissenschaften

Publish with us

Policies and ethics

Search

Navigation