Table of Content

Open Access

ARTICLE

Stream-Based Data Sampling Mechanism for Process Object

Yongzheng Lin1, Hong Liu1, ∗, Zhenxiang Chen2, Kun Zhang2, Kun Ma2
School of Information Science and Engineering, Shandong Normal University, Jinan, China.
School of Information Science and Engineering, University of Jinan, Jinan, China.
* Corresponding Author: Hong Liu. Email: .

Computers, Materials & Continua 2019, 60(1), 245-257. https://doi.org/10.32604/cmc.2019.04322

Abstract

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.

Keywords

Process object, data sampling, big data, data stream, clustering, stream processing.

Cite This Article

Y. Lin, H. Liu, Z. Chen, K. Zhang and K. Ma, "Stream-based data sampling mechanism for process object," Computers, Materials & Continua, vol. 60, no.1, pp. 245–257, 2019.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1306

    View

  • 763

    Download

  • 0

    Like

Related articles

Share Link

WeChat scan