Abstract
Process object is the abstraction of process. In process object, there are different type of entities and associations. The entities vary dependent on other entities. The performance and evolution of process object are affected by the association between entities. These changes could be reflected in the data collected from the process objects. These data from process object could be regard as big data stream. In the context of big data, how to find appropriate data for process object is a challenge. The data sampling should reflect the performance change of process object, and should be adaptive to the current underlying distribution of data in data stream. For finding appropriate data in big data stream to model process object, an adaptive data sampling mechanism is proposed in this paper. Experiments demonstrate the effectiveness of the proposed adaptive data sampling mechanism for process object.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. 61472232), Natural Science Foundation of Shandong Province of China (No. ZR2017BF016), and the Science and Technology Program of University of Jinan (No. XKY1623).
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Lin, Y., Liu, H., Chen, Z., Zhang, K., Ma, K. (2018). Adaptive Data Sampling Mechanism for Process Object. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11334. Springer, Cham. https://doi.org/10.1007/978-3-030-05051-1_18
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DOI: https://doi.org/10.1007/978-3-030-05051-1_18
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