Loading [MathJax]/extensions/MathMenu.js
Application Research for real time computing | IEEE Conference Publication | IEEE Xplore
Subscribe
ADVANCED SEARCH
Conferences >2022 Global Conference on Rob...

Application Research for real time computing

PDF
He Liu
All Authors

Abstract:

In the era of big data and artificial intelligence, the key performance indicators of real-time computing encounter many challenges. It is very difficult to realize real-...Show More

Metadata

Abstract:

In the era of big data and artificial intelligence, the key performance indicators of real-time computing encounter many challenges. It is very difficult to realize real-time computing by using traditional computing tools. This research will introduce the specific application research of Flink in real-time computing through the production and practice process of Apache Flink in real-time computing. Through the research, it is found that Flink has good performance in massive data, real-time ETL, real-time risk control, distributed call chain analysis and other application cases. Research and propose specific Flink platform construction technology methods, and put forward improvement measures for several Flink problems encountered in the process of Flink platform.
Published in: 2022 Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT)
Date of Conference: 30-31 July 2022
Date Added to IEEE Xplore: 04 October 2022
ISBN Information:
DOI: 10.1109/GCRAIT55928.2022.00049
Conference Location: Chicago, IL, USA
References is not available for this document.
Contents

I. Evolution and Challenges in Real-Time Computing

Real time calculation is an online calculation based on some disordered time series data that arrive in real time, the rate is uncontrollable, the arrival order is independent and does not guarantee the order, and cannot be replayed unless specially saved. Therefore, in real-time computing, there will be problems such as data disorder, data delay, and inconsistency between event time and processing time. The number of peak events of the platform reached 11 million / s, and it encountered considerable challenges in correctness, fault tolerance, performance, delay, throughput, scalability, etc.

Select All
1.
Apache Flink official website [EB/OL].
Google Scholar
2.
Fu Wen and Nie Qiang, real-time analysis of spark big data [M], Beijing University of Technology Press.
Google Scholar
3.
Liu He and Yang Yi, Intelligent system [M], electronic industry press, April 2020.
Google Scholar
4.
M. Yang, J. Ma, P. Wang, Z. Huang, Y. Li, H. Liu, et al., "Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model for Parkinson's Disease Speech Data", Diagnostics, vol. 11, 2021.
CrossRef Google Scholar
5.
Nie Qiang and Fu Wen, Hadoop offline analysis practice [M], Beijing University of Technology Press, 2021.
Google Scholar
6.
(German) rezal Karim (American) sridar alla Scala and spark big data analysis [M], Tsinghua University Press, June 2020.
Google Scholar
7.
Cao Jie, spark big data analysis technology [M], Beijing University of Aeronautics and Astronautics Press, 2021.
Google Scholar
8.
J Jules and Jules J. Berman, big data theory and practice [M], China Machine Press, 2020.
Google Scholar
9.
Rajendra Akerkar Norway and Priti Srinivas Sajja, big data analysis and algorithm [M], China Machine Press, October 2018.
Google Scholar
10.
Y Miao, H Tang and G. Pan, "A Supervised Multi-Spike Learning Algorithm for Spiking Neural Networks[C]", 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2018.
View Article
Google Scholar
11.
T Zhang, Y Zeng, D Zhao et al., "A plasticity-centric approach to train the non-differential Spiking neural networks[C]", Thirty-Second AAAI Conference on Artificial Intelligence. two thousand and eighteen.
CrossRef Google Scholar
12.
T Zhang, Y Zeng, D Zhao et al., "Brain-inspired Balanced Tuning for Spiking Neural Networks[C]", IJCAI, pp. 1653-1659, 2018.
CrossRef Google Scholar
13.
M Zhang, H Qu, J Li et al., "Feedforward computational model for pattern recognition with Spiking neurons[J]", International Journal of Robotics and Automation, vol. 33, pp. 206-5044, 2018.
CrossRef Google Scholar
14.
M Zhang, H Qu, A Belatreche et al., "EMPD: An efficient membrane potential driven supervised learning algorithm for Spiking neurons[J]", IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 151-162, 2018.
View Article
Google Scholar
15.
Chen Zhiyi and Sui Jie, "Integrated multimodal rumor detection method based on deepfm and convolutional neural network [J]", Computer science, vol. 49, no. 01, pp. 101-107, 2022.
Google Scholar

Contact IEEE to Subscribe
More Like This
Determination of Health Key Performance Indicators and Their Visualization in the Production System in the Context of Industry 4.0

2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA)

Published: 2021

Production Optimization Monitoring System Implementing Artificial Intelligence and Big Data

2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT

Published: 2020

Show More

References

References is not available for this document.

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.