Browsing by Author "Wang, Tien-Chun"

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An Effective Method for Top-k Dominating Query Processing over Multiple Uncertain Data Streams
(2018-01-01)
Liu, Chuan-Ming
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Wang, Tien-Chun
;
Lai, Chuan-Chi
;
Wang, Li-Chun
;
電機工程學系
This paper explores the top-k dominating query process on multiple uncertain data streams and employs the parallel computation to facilitate the query process. The challenges include how to quickly update the result and reduce the computation cost for processing uncertainty. By referring the related existing papers for certain data, we provide an effective top-k dominating query process on uncertain data streams in terms of time and space and the provided approach can be parallelized easily. After discussing the properties of the proposed approach, we validate our methods through extensive simulated experiments. The experimental results indicate that our algorithms can avoid the unnecessary computation effectively and reduce lots of communication throughput between servers, thus achieving the objective of updating the results quickly.
Probabilistic Top-k Dominating Query Monitoring Over Multiple Uncertain IoT Data Streams in Edge Computing Environments
(2019-10-01)
Lai, Chuan-Chi
;
Wang, Tien-Chun
;
Liu, Chuan-Ming
;
Wang, Li-Chun
;
電機工程學系
Extracting the valuable features and information in big data has become one of the important research issues in data science. In most Internet of Things (IoT) applications, the collected data are uncertain and imprecise due to sensor device variations or transmission errors. In addition, the sensing data may change as time evolves. We refer an uncertain data stream as a dataset that has velocity, veracity, and volume properties simultaneously. This paper employs the parallelism in edge computing environments to facilitate the top-k dominating query process over multiple uncertain IoT data streams. The challenges of this problem include how to quickly update the result for processing uncertainty and reduce the computation cost as well as provide highly accurate results. By referring to the related existing papers for certain data, we provide an effective probabilistic top-k dominating query process on uncertain data streams, which can be parallelized easily. After discussing the properties of the proposed approach, we validate our methods through the complexity analysis and extensive simulated experiments. In comparison with the existing works, the experimental results indicate that our method can improve almost 60% computation time, reduce nearly 20% communication cost between servers, and provide highly accurate results in most scenarios.