Fog computing and Big data in projects of class smart city
Електронний науковий архів Науково-технічної бібліотеки Національного університету "Львівська політехніка"
Переглянути архів ІнформаціяПоле | Співвідношення | |
Title |
Fog computing and Big data in projects of class smart city
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Creator |
Duda, O.
Kunanets, N. Matsiuk, O. Pasichnyk, V. Rzheuskyi, A. |
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Contributor |
Ternopil Ivan Puluj National Technical University
Lviv Polytechnic National University |
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Subject |
cloud technology
foggy computing Big data information technology platform smart city |
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Description |
The possibilities of using the information technology of foggy computing, which implements the processes of selecting primary messages from sensory nodes and their processing, and further transferring the results of primary computing to server environments based on information technology platforms of cloud type are analyzed in the article. It is noted that both of the approaches – mist and cloud computing – can be effectively used in a wide range of applications, in particular those that are used in information and technology complexes of “smart cities”. The system of parameters which distinguishes a separate class of information technologies called Big data is analyzed in the paper. The analysis made it possible to fix 10 basic parameters of the so-called 10 v, with the help of which a separate class of information technologies is allocated. Big data technologies, along with the technologies of foggy and cloud computing are elements of a powerful information technology platform that allows us to solve a wide range of problems for smart cities. The authors illustrated the systemtechnological connections of these classes of information technologies and analyzed the possibilities of their use in the context of implementation of the information technology project “Ternopil Smart city”. |
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Date |
2019-06-18T12:03:46Z
2019-06-18T12:03:46Z 2018-06-18 2018-06-18 |
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Type |
Article
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Identifier |
Fog computing and Big data in projects of class smart city / O. Duda, N. Kunanets, O. Matsiuk, V. Pasichnyk, A. Rzheuskyi // Econtechmod. — Lublin, 2018. — Vol 7. — No 3. — P. 7–12.
2084-5715 http://ena.lp.edu.ua:8080/handle/ntb/45132 Fog computing and Big data in projects of class smart city / O. Duda, N. Kunanets, O. Matsiuk, V. Pasichnyk, A. Rzheuskyi // Econtechmod. — Lublin, 2018. — Vol 7. — No 3. — P. 7–12. |
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Language |
en
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Relation |
Econtechmod, 3 (7), 2018
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Information Sciences 275, 314–347. 1. Gantz J., Reinsel D. 2012. The digital universe in2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future 2007, 1–16. 2. Habak K. 2017. 7 Elastic Mobile Device Clouds: Leveraging Mobile Devices to Provide Cloud Computing Services at the Edge, Fog for 5G and IoT. 3. Habak K. 2017. 7 Elastic Mobile Device Clouds: Leveraging Mobile Devices to Provide Cloud Computing Services at the Edge", Fog for 5G and IoT. 4. Chiang M. 2017. Clarifying Fog Computing and Networking: 10 Questions and Answers", IEEE Commun. Mag, Vol. 55. No. 4, 18–20. 5. Zikopoulos P. 2013. Harness the power of big data: The IBM big data platform. New York, NY: McGraw-Hill 6. Berman J. J. 2013. Principles of big data: preparing, sharing, and analyzing complex information. Newnes. 7. Gartner IT Glossary > Big Data. Available online at:https://www.gartner.com/it-glossary/big-data. 8. Gantz J., Reinsel D. 2011. Extracting value from chaos.IDC iview 1142.2011, 1–12. 9. Aazam Mohammad, Sherali Zeadally, and Khaled A. Harras. 2018. Fog Computing Architecture, Evaluation, and Future Research Directions. IEEE Communications Magazine 56.5,46–52. 10. Aazam, Mohammad, Sherali Zeadally, Khaled A. Harras. 2018. Fog Computing Architecture, Evaluation, and Future Research Directions. IEEE Communications Magazine 56.5, 46–52. 11. Aazam, Mohammad, Sherali Zeadally, Khaled A. Harras. 2018. Fog Computing Architecture, Evaluation, and Future Research Directions. IEEE Communications Magazine 56.5, 46–52. 12. Manzalini A., Crespi N., 2016. An Edge Operating System Enabling Anything-as-a-Service, IEEE Commun. Mag., Vol. 54. No. 3, 62–67. 13. Habak K. 2017. 7 Elastic Mobile Device Clouds: Leveraging Mobile Devices to Provide Cloud Computing Services at the Edge, Fog for 5G and IoT. 14. Chiang M. 2017. Clarifying Fog Computing and Networking: 10 Questions and Answers, IEEE Commun. Mag., Vol. 55. No. 4, 18–20. 15. Hong K. 2013. Mobile Fog: A Programming Model for Large-Scale Applications on the Internet of Things. Proc. 2nd ACM SIGCOMM Wksp. Mobile Cloud Computing, Aug. 2013, 15–20. 16. Hashem Ibrahim Abaker Targio. 2015. The rise of "big data" on cloud computing: Review and open research issues. Information Systems 47, 98–115. 17. Manyika J. 2011. Big data: The next frontier for innovation, competition, and productivity. 18. Demchenko Yu., Cees De Laat, Membrey P.2014. Defining architecture components of the Big Data Ecosystem. Collaboration Technologies and Systems (CTS), 2014 International Conference on. IEEE, 2014. 19. Ahn Jong Wook, Mi Sook Yi, Dong Bin Shin.2013. Study for spatial big data concept and system building. Journal of Korea Spatial Information Society 21.5, 43–51. 20. Demchenko Yu., Gruengard E., Sander Klous.2014. Instructional model for building effective Big Data curricula for online and campus education. Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on. IEEE, 2014. 21. Uddin Muhammad Fahim, Navarun Gupta. 2014. Seven V's of Big Data understanding Big Data to extract value. American Society for Engineering Education (ASEE Zone 1), 2014 Zone 1 Conference of the. IEEE, 2014. 22. Saggi Mandeep Kaur, Sushma Jain. 2018. A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management. 23. Sivarajah Uthayasankar. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research 70, 263–286. 24. Keim D., Huamin Qu, Kwan-Liu Ma. 2013. Bigdata visualization. IEEE Computer Graphics and Applications 33.4, 20–21. 25. Tsai Chun-Wie. 2015. Big data analytics: a survey. Journal of Big Data 2.1, p. 21. 26. Chen CL Philip, Chun-Yang Zhang. 2014. Dataintensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences 275, 314–347. |
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Rights |
© Copyright by Lviv Polytechnic National University 2018
© Copyright by Polish Academy of Sciences 2018 © Copyright by University of Engineering and Economics in Rzeszów 2018 © Copyright by University of Life Sciences in Lublin 2018 |
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Format |
7-12
6 application/pdf image/png |
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Coverage |
Lublin
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