nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Big Data Management and Technologies

verfasst von : Altan Cakir

Erschienen in: Business Analytics for Professionals

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

We create and collect more data today than we have in the past. All this data comes from and is reviewed from different sources, including social media platforms, our phones and computers, healthcare gadgets and wearable technology, scientific instruments, financial institutions, the manufacturing industry, news channels and more. When these small and wide data are analyzed, it offers businesses the opportunity to take quick action in business-development processes (B2B, B2C), gain a different perspective, and better understand applications, creating new opportunities. While changing their sales and marketing strategies, businesses are now able to manage the data they collect in real time to transform themselves, to record them in a healthy way, to analyze and evaluate data-based processes, and to determine their digital transformation roadmaps, their interactions with their customers, sectoral diffraction, application and advanced analyses. They want to accelerate the transformation processes within the technology triangle. As a result, big data technologies and applications are at the center of everything and becomes an important application for digital transformation.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel Prescriptive Analytics: Optimization and Modeling

Nächstes Kapitel Supply Chain Analytics

Forbes MB (2018) How much data do we create every day? https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#4146a89b60ba. Accessed on 28 Nov 2019

Ionescu A (2018) Processing petabytes of data in seconds with databricks delta. https://databricks.com/blog/2018/07/31/processing-petabytes-of-data-in-seconds-with-databricks-delta.html. Accessed 28 Nov 2019

McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton D (2012) Big data: the management revolution. Harvard Bus Rev 90(10):60–68

Pouyanfar S, Yang Y, Chen SC, Shyu ML, Iyengar SS (2018) Multimedia big data analytics: a survey. ACM Comput Surv 51(1):1–34CrossRef

Zephoria. Digital Marketing. The top 20 valuable Facebook statistics https://zephoria.com/top-15-valuable-facebook-statistics/. Accessed Nov 2018

Iafrate F (2014) A journey from big data to smart data. In: Digital enterprise design and management. Cham, Springer, pp 25–33

Borne K (2014) Top 10 big data challenges a serious look at 10 big data v’s. https://mapr.com/blog/top-10-big-data-challenges-serious-look-10-big-data-vs. Accessed 11 Apr 2014

Lenk A, Bonorden L, Hellmanns A, Roedder N, Jaehnichen S (2015) Towards a taxonomy of standards in smart data. IEEE international conference on big data (Big Data). Piscataway, IEEE, pp 1749–1754CrossRef

Tsai CW, Lai CF, Chao HC, Vasilakos AV (2015) Big data analytics: a survey. J Big Dat 2(1):1–32CrossRef

10.

Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute

11.

Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Netw Appl 19(2):171–209CrossRef

12.

Iafrate F (2014) A journey from big data to smart data. Digital enterprise design and management. Springer, Cham, pp 25–33CrossRef

13.

Ma C, Zhang HH, Wang X (2014) Machine learning for big data analytics in plants. Trends Plant Sci 19(12):798–808CrossRef

14.

Saidulu D, Sasikala R (2017) Machine learning and statistical approaches for big data: issues, challenges and research directions. Int J Appl Eng Res 12(21):11691–11699

15.

Laney D (2001) 3D data management: controlling data volume, velocity and variety. META Group Res Note 6(70):1

16.

Jain A (2017) The 5 Vs of big data. IBM Watson Health Perspectives. https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data/. Accessed 30 May 2017

17.

IBM big data and analytics hub. Extracting Business Value from the 4 V’s of Big Data (2016) http://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data

18.

Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35(2):137–144CrossRef

19.

Marr B (2017) Really big data at Walmart: real-time insights from their 40+ Petabyte data cloud. https://www.forbes.com/sites/bernardmarr/2017/01/23/really-big-data-at-walmart-real-time-insights-from-their-40-petabyte-data-cloud/#2a0c16916c10

20.

Pokorný J, Škoda P, Zelinka I, Bednárek D, Zavoral F, Kruliš M, Šaloun P (2015) Big data movement: a challenge in data processing. In: Big data in complex systems. Cham, Springer, pp 29–69

21.

Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, AmsterdamMATH

22.

Xiong H, Pandey G, Steinbach M, Kumar V (2006) Enhancing data analysis with noise removal. IEEE Trans Knowl Data Eng 18(3):304–319CrossRef

23.

Court D (2015) Getting big impact from big data. McKinsey Q 1:52–60

24.

Golchha N (2015) Big data—the information revolution. IJAR 1(12):791–794

25.

Khan M, Ayyoob M (2018) Big data analytics evaluation. Int J Eng Res Comput Sci Eng (IJERCSE) 5(2):25–38

26.

Wang X, He Y (2016) Learning from uncertainty for big data: future analytical challenges and strategies. IEEE Syst Man Cybern Mag 2(2):26–31CrossRef

27.

Jordan MI (2012) Divide-and-conquer and statistical inference for big data. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, p 4

28.

Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422CrossRef

29.

Liu H, Motoda H (eds) (2007) Computational methods of feature selection. CRC Press, Boca RatonMATH

30.

Belhadi A, Zkik K, Cherrafi, A, Yusof SM, fezazi SE (2019) Understanding the capabilities of big data analytics for manufacturing process: insights from literature review and multiple case study. Comput Ind Eng 137. https://doi.org/10.1016/j.cie.2019.106099

31.

IBM Analytics: IBM Industry Model support for a data lake architecture (2016). https://www.ibm.com/downloads/cas/DNKPJ80Q. Accessed 20 Feb 2021

32.

Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–169. https://doi.org/10.1016/j.jmsy.2018.01.006CrossRef

33.

Shao G, Jain S, Shin SJ (2014) Data analytics using simulation for smart manufacturing. Proc Winter Simul Conf. https://doi.org/10.1109/WSC.2014.7020063CrossRef

34.

Syafrudin M, Fitriyani NL, Li D, Alfian G, Rhee J, Kang YS (2017) An open source-based real-time data processing architecture framework for manufacturing sustainability. Sustainability 9(11). https://doi.org/10.3390/su9112139

35.

Dai HN, Wang H, Xu G, Wan J (2019) Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies. Enterprise Inf Syst 14(6). https://doi.org/10.1080/17517575.2019.1633689

36.

Wilcox T, Jin N, Flach P, Thumim J (2019) A big data platform for smart meter data analytics. Comput Ind 105:250–259. https://doi.org/10.1016/j.compind.2018.12.010CrossRef

37.

https://en.wikipedia.org/wiki/ApacheNiFi. Accessed 20 Feb 2021

38.

Pandya A, Kostakos P, Mehmood H, Cortes M (2019) Privacy preserving sentiment analysis on multiple edge data streams with apache nifi. In: Proceedings of European intelligence and security informatics conference (EISIC). https://doi.org/10.1109/EISIC49498.2019.9108851

39.

Kreps J, Narkhede N, Rao J (2011) Kafka: a distributed messaging system for log processing. In: Proceedings of the NetDB, Athens, Greece

40.

https://kafka.apache.org. Accessed 20 Feb 2021

41.

Mu C, Zhao J, Yang G, Zhang J, Yan Z (2018) Towards practical visual search engine within elasticsearch. 1806.08896

42.

Srivastava A, Miller D (2019) Elasticsearch 7 quick start guide. Packt Publishing

43.

Ku ́c R, Rogozin ́ski M (2015) Mastering Elasticsearch. Packt Publishing

44.

Kuc R, Rogozinski M (2014) Elasticsearch Server. Packt Publishing

45.

Srivastava A, Azarmi B (2019) Learning Kibana 7: build powerful elastic dashboards with Kibana’s data. Packt Publishing

46.

Build visualizations simply and intuitively. https://www.elastic.co/kibana. Accessed 20 Feb 2021

47.

Flask Web Development, one drop at a time. http://flask.pocoo.org Accessed 20 Feb 2021

48.

Erraissi A, Belangour A, Tragha A (2017) A big data hadoop building blocks comparative study. Int J Comput Trends Technol 48(1):36–40. https://doi.org/10.14445/22312803/IJCTT-V48P109CrossRef

49.

Chiary MR, Anand R (2015) Hadoop cluster on linode using ambari for improving task assignment scheme running in the clouds. Int J Comput Sci Inf Technol 6(1):586–589

50.

Salloum S, Dautov R, Chen X, Peng PX, Huang JZ (2016) Big data analytics on apache spark. Int J Data Sci Anal 1:145–164CrossRef

51.

Shoro AG, Soomro TR (2015) Big data analysis: Apache spark perspective. Global J Comput Sci Technol 15(1)

52.

Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Zaharia M (2009) Above the clouds: a berkeley view of cloud computing. Technical Report UCB/EECS-2009-28, vol. 17. EECS Department, University of California, Berkeley

53.

https://databricks.com. Accessed 20 Feb 2021

54.

Beauchemin M Airflow: a workflow management platform. https://medium.com/airbnb-engineering/airflow-aworkflow-management-platform-46318b977fd8. Accessed 20 Feb 2021

55.

Gantz J, Reinsel D (2011) Extracting value from chaos. IDC iview 1142:1–12

56.

https://nifi.apache.org. Accessed 20 Feb 2021

57.

Samal B, Panda M (2017) Real time product feedback review and analysis using apache technologies and nosql database. Int J Eng Comput Sci 6(10):22551–22558. https://doi.org/10.18535/ijecs/v6i10.04CrossRef

58.

Soner K, Upadhyay H (2014) A survey: Ddos attack on internet of things. Int J Eng Res Dev 10(11):58–63

59.

What is Elasticsearch. https://www.elastic.co/guide/en/elasticsearch/reference/master/elasticsearch-intro.html. Accessed 20 Feb 2021

Titel: Big Data Management and Technologies
verfasst von: Altan Cakir
Verlag: Springer International Publishing
Buch: Business Analytics for Professionals
Print ISBN: 978-3-030-93822-2

Electronic ISBN: 978-3-030-93823-9

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-93823-9_10

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"