Computer Science
Efficient and Robust Top-k Algorithms for Big Data IoT
Document Type
Conference Paper
Abstract
Top-k considers as a technique to retrieve, from a hypothetically big data set, only the \mathrm{k}(k\geq 1) best (most relevant/important) candidates. Top-k query processing is a decisive necessity in various collaborative environments that comprise big data such as the Internet of Things (IoT) networks. Particularly, efficient top-k processing in large-scale distributed systems has shown a positively noticeable effect on their performance. This paper considers the distributed approximate top-k processing algorithms dedicated to the IoT-based networks and improve the accuracy of algorithms introduced previously. We then propose a safety-based fault-tolerance notation and contribute to improving a known algorithm in terms of accuracy. Our algorithms have been evaluated using simulation and real-world data and show superiority over conventional methods.
Publication Title
IEEE International Conference on Communications
Publication Date
6-2020
Volume
2020
ISSN
1550-3607
ISBN
9781728150895
DOI
10.1109/ICC40277.2020.9148639
Keywords
approximate query, distributed algorithms, fault-tolerance, IoT, Top-K query
Repository Citation
Yang, Ruifan; Zhou, Zheng; Tseng, Lewis; Aloqaily, Moayad; and Boukerche, Azzedine, "Efficient and Robust Top-k Algorithms for Big Data IoT" (2020). Computer Science. 124.
https://commons.clarku.edu/faculty_computer_sciences/124
APA Citation
Yang, R., Zhou, Z., Tseng, L., Aloqaily, M., & Boukerche, A. (2020, June). Efficient and robust top-k algorithms for big data iot. In ICC 2020-2020 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
