Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis


  • Jianhu Gong School of Data and Computer Science, Guangdong Peizheng College, Guangzhou 510830, P.R. China



Cache, prefetch, unordered pages, association analysis, data mining


The purpose of this paper is to research on the design and analysis of low delay deterministic network based on data mining association. This paper studies and implements the algorithm of mining page association rules. A session recognition algorithm based on log reference page and request time is proposed by using the time probability relationship of continuous requests. This method improves the accuracy of log data preprocessing and page association rules mining. This paper studies and tests the efficiency, prefetch timing and cache organization of the two page association rules. The results show that in this prefetch scheme, the prefetch performance of the association rules of unordered pages is better than that of the association rules of ordered pages. The prefetch performance when cache hits is better than that when cache fails and the cache fails to hit has better performance. In the case of a certain size of cache space, reasonable organization of cache space can further improve the cache hit rate and reduce the network delay.


Download data is not yet available.

Author Biography

Jianhu Gong, School of Data and Computer Science, Guangdong Peizheng College, Guangzhou 510830, P.R. China

Jianhu Gong has been engaged in teaching in the school of data and computer science of Guangdong Peizheng College since he got his doctor’s degree from City University of Macau in 2013. He has published many papers in the fields of computer network, data science and big data technology, and participated in the research work of the Ministry of Education, Guangdong Province and other departments. He has obtained many authorized patents and software copyrights Item.


Prasan S, Sudhir P, Shih-Lin W. Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks. Sensors, 2017, 17(10):2185–2193.

Anwar M, Xia Y, Zhan Y. TDMA-Based IEEE 802.15.4 for Low-Latency Deterministic Control Applications. Industrial Informatics IEEE Transactions on, 2016, 12(1):338–347.

Janssen J, Vleeschauwer DD, Petit GH, et al. Delay Bounds for Voice over IP Calls Transported over Satellite Access Networks. Mobile Networks & Applications, 2002, 7(1):79–89.

Al-Nidawi Y, Yahya H, Kemp AH. Tackling Mobility in Low Latency Deterministic Multihop IEEE 802.15.4e Sensor Network. IEEE Sensors Journal, 2015, 16(5):1–10.

Hu L, Cao X, Li Z. Reliability analysis of discrete time redundant system with imperfect switch and random uncertain lifetime. Journal of Intelligent and Fuzzy Systems, 2019, 37(1):1–12.

Zhang P, Huang J, Zhou Z, et al. Joint category-level and discriminative feature learning networks for unsupervised domain adaptation. Journal of Intelligent and Fuzzy Systems, 2019, 37(3):1–12.

Reinhold R, Underberg L, Wulf A, et al. Industrial WSN Based on IR-UWB and a Low-Latency MAC Protocol. Frequenz, 2016, 70(7–8):17–26.

Whitt, Ward. Fluid Models for Multiserver Queues with Abandonments. Operations Research, 2006, 54(1):37–54.

Chen M, Zhang J, Murthi MN, et al. Delay-based TCP congestion avoidance: A network calculus interpretation and performance improvements. Computer Networks, 2009, 53(9):1319–1340.

Ouanteur C, Aissani D, Bouallouche-Medjkoune L, et al. Modeling and performance evaluation of the IEEE 802.15.4e LLDN mechanism designed for industrial applications in WSNs. Wireless Networks, 2017, 23(5):1343–1358.

Golden BL, Magnanti TL. Deterministic Network Optimization: A Bibliography. Networks, 2006, 7(2):149–179.

Tainiter M. A new deterministic network reliability measure. Networks, 1976, 6(3):191–204.

Yang J, Reichert P, Abbaspour KC, et al. Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference. Journal of Hydrology, 2007, 340(3–4):167–182.

Wang XL, Cai XD, Su ZE, et al. Quantum teleportation of multiple degrees of freedom of a single photon. Nature, 2015, 518(7540):516–519.

Kellogg RA, Gómez-Sjuberg, Rafael, Leyrat AA, et al. High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics. Nature Protocols, 2014, 9(7):1713–1726.

Elsayed, K. M. F. A framework for end-to-end deterministic-delay service provisioning in multiservice packet networks. IEEE Transactions on Multimedia, 2005, 7(3):563–571.

Ojo MO, Giordano S, Adami D, et al. Throughput Maximizing and Fair Scheduling Algorithms in Industrial Internet of Things Networks. IEEE Transactions on Industrial Informatics, 2018:1–12.

Sensoy M, Yilmaz B, Yiltnaz E. An Intelligent Packet Loss Control Heuristic for Connectionless Real-Time Voice Communication. Mathematical Problems in Engineering, 2010, 2010(PT.1):p.48.1–48.9.

Li Y. The Prediction Research of End-to-End Delay Upper Bound for Expedited Forwarding. signal processing, 2009.

Andrews M, Zhang L. Creating templates to achieve low delay in multi-carrier frame-based wireless data systems. Wireless Networks, 2010, 16(6):1765–1776.

Masoudi-Sobhanzadeh Y, Masoudi-Nejad A. Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm. BMC bioinformatics, 2020, 21(1):1–21.

Wang C, Zheng X. Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint. Evolutionary Intelligence, 2020, 13(1):39–49.

Moslehi F, Haeri A, Martínez-Álvarez F. A novel hybrid GA–PSO framework for mining quantitative association rules. Soft Computing, 2020, 24(6):4645–4666.

Shao Z, Li Y, Wang X, et al. Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining. Journal of Ambient Intelligence and Humanized Computing, 2020, 11(2):539–551.

Zheng Q, Li Y, Cao J. Application of data mining technology in alarm analysis of communication network. Computer Communications, 2020, 163:84–90.

He Z, Tao L, Xie Z, et al. Discovering spatial interaction patterns of near repeat crime by spatial association rules mining. Scientific reports, 2020, 10(1):1–11.

Wang C, Bian W, Wang R, et al. Association rules mining in parallel conditional tree based on grid computing inspired partition algorithm. International Journal of Web and Grid Services, 2020, 16(3):321–339.






Advanced Practice in Web Engineering