JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (10): 7-14.doi: 10.6040/j.issn.1671-9352.0.2020.178
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ZHANG Ling, REN Xue-fang*
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[1] 史开泉. 大数据结构-逻辑特征与大数据规律[J] ,山东大学学报(理学版), 2019, 54(2):1-29. SHI Kaiquan. Big data structure-logic characteristics and big data law[J]. Journal of Shandong University(Naturel Science), 2015, 54(2):1-29. [2] 史开泉. P-集合[J]. 山东大学学报(理学版), 2008, 43(11):77-84. SHI Kaiquan. P-sets[J]. Journal of Shandong University(Natural Science), 2008, 43(11):77-84. [3] SHI Kaiquan. P-sets and its applications[J]. Advances in Systems Science and Applications, 2009, 9(2):209-219. [4] 史开泉. P-推理与信息的P-推理发现-辨识[J]. 计算机科学, 2011, 38(7):1-9. SHI Kaiquan. P-reasoning and P-reasoning discovery-identification of information[J]. Computer Science, 2011, 38(7):1-9. [5] 史开泉. P-集合, 逆P-集合与信息智能融合-过滤辨识[J]. 计算机科学,2012, 39(4):1-13. SHI Kaiquan. P-sets, inverse P-sets and the intelligent fusion-filter identification of information[J]. Computer Science, 2012, 39(4):1-13. [6] FAN Chengxian, LIN Hongkang. P-sets and the reasoning-identification of disaster information[J]. International Journal of Convergence Information Technology, 2012, 7(1):337-345. [7] 史开泉. 函数P-集合[J]. 山东大学学报(理学版), 2011, 46(2):62-69. SHI Kaiquan. Function P-sets[J]. Journal of Shandong University(Natural Science), 2011, 46(2):62-69. [8] SHI Kaiquan. Function P-sets[J]. International Journal of Machine Learning and Cybernetics, 2011, 2(4):281-288. [9] TANG Jihua, ZHANG Ling, SHI Kaiquan. Outer P-information law reasoning and its application in intelligent fusion and separating of information law[J]. Microsystem Technologies, 2018, 24(10):4389-4398. [10] 张凌, 任雪芳, 史开泉. 信息规律智能变换-伪装与P-规律增广矩阵[J]. 山东大学学报(理学版), 2016, 51(8):90-97. ZHANG Ling, REN Xuefang, SHI Kaiquan. Intelligent switch-camouflage of information laws and P-law augmented matrices[J]. Journal of Shandong University(Natural Science), 2016, 51(8):90-97. [11] 史开泉. 逆P-集合[J]. 山东大学学报(理学版), 2012, 47(1):98-109. SHI Kaiquan. Inverse P-sets[J]. Journal of Shandong University(Natural Science), 2012, 47(1):98-109. [12] 史开泉. 函数逆P-集合与信息规律融合[J]. 山东大学学报(理学版), 2012, 47(8):73-80. SHI Kaiquan. Function inverse P-sets and information law fusion[J]. Journal of Shandong University(Natural Science), 2012, 47(8):73-80. [13] 任雪芳, 张凌, 史开泉. 基数余-亏与逆P-增广矩阵[J]. 山东大学学报(理学版), 2015, 50(10):13-18, 26. REN Xuefang, ZHANG Ling, SHI Kaiquan. Surplus-deficiency of cardinal number and inverse P-augmented matrices[J]. Journal of Shandong University(Natural Science), 2015, 50(10):13-18, 26. [14] 张凌, 任雪芳. 基数余-亏定理与数据外-内挖掘-分离[J]. 山东大学学报(理学版), 2015, 50(8):90-94. ZHANG Ling, REN Xuefang. Surplus-deficient theorem of cardinal number and data internal-outer mining-separation[J]. Journal of Shandong University(Natural Science), 2015, 50(8):90-94. [15] ZHANG Ling, REN Xuefang, SHI Kaiquan. The dynamic segmentation characteristics of P-augmented matrix and the dynamic intelligent acquisition of P-information[J]. International Journal of Applied Decision Sciences, 2016, 9(4):413-425. [16] REN Xuefang, ZHANG Ling, SHI Kaiquan, et al. Inverse P-augmented matrix method-based the dynamic findings of unknown information[J]. Microsystem Technologies, 2018, 24(10):4187-4192. [17] ZHANG Ling, REN Xuefang, SHI Kaiquan. Inverse P-information law models and the reality-camouflage intelligent transformations of information image[C] //Proceedings of the 2016 International Conference on Network and Information Systems for Computers. Piscataway, NJ: IEEE, 2016: 337-341. [18] PIKE R, DORWARD S, GRIESEMER R. Interpreting the data: parallel analysis with Sawzall[J]. Scientific Programming, 2005, 13(4):277-298. [19] LUO T, LEE R, MESNIER M. hStorage-DB: heterogeneity-aware data management to exploit the full capability of hybrid storage systems[J]. Proceedings of the VLDB Endowment, 2012, 5(10):1076-1087. [20] CHEN S. Cheetah: a high performance, custom data warehouse on top of mapreduce[J]. Proceedings of the VLDB Endowment, 2010, 3(1/2):1459-1468. [21] LAM W, LIU L, PRASADST S. Muppet mapreduce-style processing of fast data[J]. Proceedings of the VLDB Endowment, 2012, 5(12):1814-1825. [22] GOODHOPE K, KOSHY J, KREPS J. Building linkedins real-time activity data pipeline[J]. IEEE Data Eng Bull, 2012, 35(2):33-45. [23] LV Y, DUAN Y, KANG W. Traffic flow prediction with big data: a deep learning approach[J]. IEEE Transactions Intelligent Transportation Systems, 2015, 16(2):865-873. |
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