https://oldena.lpnu.ua/handle/ntb/52467| Title: | One Approach of Approximation for Incoming Data Stream in IoT based Monitoring System |
| Authors: | Alieksieiev, Vladyslav |
| Affiliation: | Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | Alieksieiev V. One Approach of Approximation for Incoming Data Stream in IoT based Monitoring System / Vladyslav Alieksieiev // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 94–97. — (Big Data & Data Science Using Intelligent Approaches). |
| Bibliographic description (International): | Alieksieiev V. One Approach of Approximation for Incoming Data Stream in IoT based Monitoring System / Vladyslav Alieksieiev // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 94–97. — (Big Data & Data Science Using Intelligent Approaches). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | data compression approximation algorithm data stream processing IoT platform big data |
| Number of pages: | 4 |
| Page range: | 94-97 |
| Start page: | 94 |
| End page: | 97 |
| Abstract: | IoT devices and platforms are a fast growing market. One can mention a number of businesses relying on easy opportunity to build real-time monitoring systems using modern software and IoT hardware solutions. However, the growth has revealed a number of complex problems. Many problems are in area of data processing and storing huge volumes of information. Due to wide use of different kinds of sensors, and even a sets of sensors within each single device, on one hand, practitioners discover unpleasant effects of data losses caused by data packages losses or delays while its transition from sensor to server. On the other hand, huge volumes of data require to use some big data approaches and many startup projects feel the problem of lack of resources. Many of them feel lack of data storage facilities or become unable to support huge data sets due to lack of finance. The paper is focused to research the problem approximation for incoming data stream to make it smaller the volume of data to be stored but to keep it possible to be used. A few approaches to use such data compression via its approximation are discussed with application to IoT based real-time monitoring system. |
| URI: | https://ena.lpnu.ua/handle/ntb/52467 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | https://arxiv.org/pdf/1109.2378.pdf |
| References (Ukraine): | [1] A. Oram. Scaling Data Science for the Industrial Internet of Things. O’Reily, 2017. [2] Y. Lin, and W. Xiao. Implementing a Smart Data Platform: How Enterprises Survive in the Era of Smart Data. O’Reily, 2017. [3] T. Dunning, and E. Friedman. Time Series Databases: New Ways to Store and Access Data. O’Reily, 2015. [4] V. Alieksieiev, and O. Gaiduchok, “About the problem of data losses in real-time IoT based monitoring systems,” Proceedings of International Scientific Conference “Mathematical Modeling” (Borovets, Bulgaria, December 13–16, 2017), STUME “Industry 4.0”, Sofia, Bulgaria, Year I, vol. 1/1, pp.10–11, 2017 [5] C. J. Date. Database in Depth: Relational Theory for Practitioners. O’Reilly, CA, 2005. [6] V. Alieksieiev, G. Ivasyk, V. Pabyrivskyi, and N. Pabyrivska, “Big data aggregation algorithm for storing obsolete data,” Proceedings of International Scientific Conference “High Technologies. Business. Society 2018” (Borovets, Bulgaria, March 12–15, 2018), STUME “Industry 4.0”, Sofia, Bulgaria, Year II, iss. 1 (3), vol. I “High Technologies”, pp.113–115, 2018. [7] P. Bruce, A. Bruce, Practical Statistics for Data Scientists. O’Reily, 2017. [8] M. Milton. Head First Data Analysis. O’Reily, 2009.. [9] A. B. Downey. Think Stats. O’Reily, 2015. [10] A. Jain, M. Murty, and P. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999. [11] D. Müllner, “Modern hierarchical, agglomerative clustering algorithms,” ArXiv.org, 2011. – https://arxiv.org/pdf/1109.2378.pdf [12] S. W. Smith. The Scientist and Engineer's Guide to Digital Signal Processing. California Technical Publishing, 1997. [13] N. V. Myasnikova, M. P. Beresten, and M. P. Stroganov, “Approximation of multi extremum functions and its applications to technical systems,” Herald of higher education institutions. Volga region. Engineering sciences, no. 2 (18), pp.113–119, 2011. [In Russian] |
| References (International): | [1] A. Oram. Scaling Data Science for the Industrial Internet of Things. O’Reily, 2017. [2] Y. Lin, and W. Xiao. Implementing a Smart Data Platform: How Enterprises Survive in the Era of Smart Data. O’Reily, 2017. [3] T. Dunning, and E. Friedman. Time Series Databases: New Ways to Store and Access Data. O’Reily, 2015. [4] V. Alieksieiev, and O. Gaiduchok, "About the problem of data losses in real-time IoT based monitoring systems," Proceedings of International Scientific Conference "Mathematical Modeling" (Borovets, Bulgaria, December 13–16, 2017), STUME "Industry 4.0", Sofia, Bulgaria, Year I, vol. 1/1, pp.10–11, 2017 [5] C. J. Date. Database in Depth: Relational Theory for Practitioners. O’Reilly, CA, 2005. [6] V. Alieksieiev, G. Ivasyk, V. Pabyrivskyi, and N. Pabyrivska, "Big data aggregation algorithm for storing obsolete data," Proceedings of International Scientific Conference "High Technologies. Business. Society 2018" (Borovets, Bulgaria, March 12–15, 2018), STUME "Industry 4.0", Sofia, Bulgaria, Year II, iss. 1 (3), vol. I "High Technologies", pp.113–115, 2018. [7] P. Bruce, A. Bruce, Practical Statistics for Data Scientists. O’Reily, 2017. [8] M. Milton. Head First Data Analysis. O’Reily, 2009.. [9] A. B. Downey. Think Stats. O’Reily, 2015. [10] A. Jain, M. Murty, and P. Flynn, "Data Clustering: A Review," ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999. [11] D. Müllner, "Modern hierarchical, agglomerative clustering algorithms," ArXiv.org, 2011, https://arxiv.org/pdf/1109.2378.pdf [12] S. W. Smith. The Scientist and Engineer's Guide to Digital Signal Processing. California Technical Publishing, 1997. [13] N. V. Myasnikova, M. P. Beresten, and M. P. Stroganov, "Approximation of multi extremum functions and its applications to technical systems," Herald of higher education institutions. Volga region. Engineering sciences, no. 2 (18), pp.113–119, 2011. [In Russian] |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Alieksieiev_V-One_Approach_of_Approximation_94-97.pdf | 279.76 kB | Adobe PDF | View/Open | |
| 2018_Alieksieiev_V-One_Approach_of_Approximation_94-97__COVER.png | 597.03 kB | image/png | View/Open |
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