Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2022

05.11.2021

Big Data Knowledge Discovery as a Service: Recent Trends and Challenges

verfasst von: Neelam Singh, Devesh Pratap Singh, Bhasker Pant

Erschienen in: Wireless Personal Communications | Ausgabe 2/2022

Einloggen

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

search-config
loading …

Abstract

Current era is witnessing data explosion being generated from a wide range of resources including RFID (Radio-frequency identification), sensors, web logs, social media, IoT (Internet of Things) devices and many more. Pace at which data is being generated routinely in all the task performed by us has overwhelmed the proficiency and working of present infrastructure and analytical solutions available. Data has become the driving force of economy and has been treated as an asset for an organization. It contains truth or facts that can be interpreted and manipulated to gain insight for knowledge discovery. To excel out in competition enterprises are escalating their big data projects for knowledge discovery to gain valuable insights. These projects require scalable architectures for storage and data processing. Data-centric technologies are gaining impetus which can be provisioned as service to the organizations. Cloud computing is an effective and promising solution for refined analytical application. Cloud computing model supports resources to be provisioned as service. Herein paper we examine the requirements for provisioning Big Data Knowledge Discovery as a service. In addition, we explore the prevalent big data frameworks accessible and provisioned as a service via cloud. We also explore the state-of-the- art progress in this arena with open challenges and research prospects.
Vorheriger Artikel Network Lifetime Enhancement of Wireless Sensor Networks Using EFRP Protocol
Nächster Artikel ESHARP: Energy-Efficient and Smart Hierarchical Routing Protocol Based on Smart Slumber for Wireless Sensor Networks

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

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+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 "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
Literatur
1.
Manyika, J., Chui, M., Brown, B., Bughin, J., et al. (2011). Big Data: The next frontier for innovation, competition, and productivity. Technical report, McKinsey Global Institute.
2.
Singh, N., Singh, D. P., & Pant, B. A. (2017). Comprehensive Study of big data machine learning approaches and challenges. In Proceedings of the International Conference on Next Generation Computing and Information Systems (ICNGCIS); 2017 Dec 11–12; MIET Jammu, India: IEEE; pp. 80–85.
3.
Cardoso, A., & Simões, P. (2011). Cloud computing: Concepts, technologies and challenges. In: International Conference on Virtual and Networked Organizations, Emergent Technologies, and Tools; Jul: Springer, Berlin, and Heidelberg, pp. 127–136.
4.
Math, R. (2018). Big Data Analytics: Recent and Emerging Application in Services Industry. Big Data Analytics. Springer.
5.
Chebbi, I., Wadii, B., & Imed, R. F. (2015). Big Data: Concepts, Challenges and Applications. Computational Collective Intelligence. Springer.
6.
Skourletopoulos, G., Mavromoustakis, C.X., Mastorakis, G., Batalla, J.M., Dobre, C., Panagiotakis, S., & Pallis, E. (2017). Big data and cloud computing: A survey of the state-of-the-art and research challenges. In Advances in Mobile Cloud Computing and Big Data in the 5G Era, Springer, 23–41.
7.
Singh, N., Singh, D. P., & Pant, B. (2019). Big data knowledge discovery platforms: A 360 degree perspective. International Journal of Engineering and Advanced Technology (IJEAT), 9(2), 2424–2433.CrossRef
8.
Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. Gaithersburg, MD: National Institution of Standards and Technology (NIST).
9.
Elshawi, R., Sakr, S., Talia, D., & Trunfio, P. (2018). Big data systems meet machine learning challenges: Towards big data science as a service. Big Data Research, 14, 1–11.CrossRef
10.
Wang, X., Yang, L. T., Liu, H., & Deen, M. J. (2017). A big data-as-a-service framework: State-of-the-art and perspectives. IEEE Transactions on Big Data, 4(3), 325–340.CrossRef
11.
Buxton, B., Goldston, D., Doctorow, C., & Waldrop, M. (2008). Big data: Science in the petabyte era. Nature, 455(7209), 8–9.CrossRef
12.
Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652–687.CrossRef
13.
Sakr, S. (2014). Cloud-hosted databases: technologies, challenges and opportunities. Cluster Computing, 17, 487–502.CrossRef
14.
Sakr, S. (2016). Big Data 2.0 Processing Systems: A Survey. Springer.CrossRef
15.
Sarkar, D. (2014). Introducing hdinsight. Pro Microsoft HDInsight. Apress.CrossRef
16.
Nadipalli, R. (2015). HDInsight Essentials. London: Packt Publishing Ltd.
17.
Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431–448.CrossRef
18.
Khan, S., Kashish, A. S., & Mansaf, A. (2018). Cloud-Based Big Data Analytics: A Survey of Current Research and Future Directions Big Data Analytics. Springer.
19.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98–115.CrossRef
20.
Khan, S., Shakil, K. A., & Alam, M. (2018). Cloud-Based Big Data Analytics: A Survey of Current Research and Future Directions. Big Data Analytics. Springer.
21.
Talia, D., Trunfio, P., & Marozzo, F. (2016). Data Analysis in the Cloud. Elsevier.
22.
Gulabani, S. (2017). Practical Amazon EC2, SQS, Kinesis, and S3.
23.
Kumar, V.D.A. et al. (2017). Cloud enabled media streaming using Amazon Web Services. In 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM). IEEE.
24.
Gonzales, J.U., & Krishnan, S.P.T. (2015). Building your next big thing with Google Cloud Platform. Aprés 27.
25.
Krishnan, S. P. T., & Jose, L. U. G. (2015). Google BigQuery. Building Your Next Big Thing with Google Cloud Platform. Apress.CrossRef
26.
27.
Serrano, N., Gallardo, G., & Hernantes, J. (2015). Infrastructure as a service and cloud technologies. IEEE Software, 32(2), 30–36.CrossRef
28.
29.
Klein, S. (2017). IoT Solutions in Microsoft’s Azure IoT Suite. Apress.CrossRef
30.
Reagan, R., & Cosmos, D. B. (2018). Web Applications on Azure. Apress.CrossRef
31.
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters Communications of the ACM cessing. Communications of the ACM, 59(11), 56–65.
32.
33.
A. Team (2016). AzureML: Anatomy of a machine learning service. In Proceedings of the 2nd International Conference on Predictive APIs and Apps, pp. 1–13.
34.
Brown, P.G. (2010). Overview of SciDB: Large scale array storage, processing and analysis. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, ACM, pp. 963–968
35.
Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., García, Á. L., Heredia, I., & Hluchý, L. (2019). Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: A survey. Artificial Intelligence Review, 52(1), 77–124.CrossRef
36.
Thusoo, A., Sarma, J. S., Jain, N., Shao, Z., Chakka, P., Anthony, S., & Murthy, R. (2009). Hive: A warehousing solution over a map-reduce framework. Proceedings of the VLDB Endowment, 2(2), 1626–1629.CrossRef
37.
George, L. (2011). Hbase: The Definitive Guide. O’Reilly Media Inc.
38.
Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. HotCloud, 10(10–10), 95.
39.
Hewitt, E. (2010). Cassandra: the Definitive Guide. O’Reilly Media Inc.
40.
Franciscus, N., Ren, X., & Stantic, B. (2018). Precomputing architecture for flexible and efficient big data analytics. Vietnam Journal of Computer Science, 5(2), 133–142.CrossRef
41.
Sakr, S., Orakzai, F. M., Abdelaziz, I., & Khayyat, Z. (2016). Large-Scale Graph Processing Using Apache Giraph. Springer.CrossRef
42.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information sciences, 275, 314–347.CrossRef
43.
Brownlee, J. (2014). BigML review: Discover the clever features in this machine learning as a service platform, 11.
44.
Redavid, D., Malerba, D., Di Martino, B., Esposito, A., Ardagna, C.A., Bellandi, V., & Damiani, E. (2018). Semantic support for model based big data analytics-as-a- service (MBDAaaS). In Conference on Complex, Intelligent, and Software Intensive Systems, Springer, Cham, pp. 1012–1021.
45.
Siddiqui, T., Shadab A.S., & Najeeb A.K. (2019). Comprehensive analysis of container technology. In 2019 4th International Conference on Information Systems and Computer Networks (ISCON), IEEE.
46.
Zheng, Z., Zhu, J., & Lyu, M.R. (2013). Service-generated big data and big data-as-a- service: An overview. In 2013 IEEE International Congress on Big Data, IEEE, pp. 403–410.
47.
Xu, X., Sheng, Q. Z., Zhang, L. J., Fan, Y., & Dustdar, S. (2015). From big data to big service. Computer, 7, 80–83.CrossRef
48.
Talia, D. (2013). Clouds for scalable big data analytics. Computer, 5, 98–101.CrossRef
49.
Ardagna, C.A., Ceravolo, P., & Damiani, E. (2016). Big data analytics as-a-service: Issues and challenges. In 2016 IEEE International Conference on Big Data (Big Data), IEEE, pp. 3638–3644.
50.
Ahmad, I., et al. (2020). Machine learning meets communication networks: Current trends and future challenges. IEEE Access, 8, 223418–223460.CrossRef
51.
Nykvist, C., et al. (2020). A lightweight portable intrusion detection communication system for auditing applications. International Journal of Communication Systems, 33(7), e4327.CrossRef
52.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59.CrossRef
Metadaten
Titel
Big Data Knowledge Discovery as a Service: Recent Trends and Challenges
verfasst von
Neelam Singh
Devesh Pratap Singh
Bhasker Pant
Publikationsdatum
05.11.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-09213-5

Weitere Artikel der Ausgabe 2/2022

Wireless Personal Communications 2/2022 Zur Ausgabe

OriginalPaper

A Hybrid Clustering Approach Based Q-Leach in TDMA to Optimize QOS-Parameters

OriginalPaper

A Miniaturized Frequency Reconfigurable Patch Antenna for IoT Applications

OriginalPaper

Swarm Based Hybrid ACO-PSO Meta-Heuristic (HAPM) for QoS Multicast Routing Optimization in MANETs

ReviewPaper

A Review of Blockchain-Based Applications and Challenges

OriginalPaper

A Lightweight Image Encryption Algorithm for Secure Communications in Multimedia Internet of Things

OriginalPaper

A Collaborative Abstraction Based Email Spam Filtering with Fingerprints

Neuer Inhalt

    Bildnachweise