Top

Cluster Computing

Published in:

27-01-2018

Access patterns mining from massive spatio-temporal data in a smart city

Authors: Lian Xiong, Xiaojun Liu, Daixin Guo, Zhihua Hu

Published in: Cluster Computing | Special Issue 3/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Facing with massive spatio-temporal data, the traditional pattern mining methods fail to directly reflect the spatio-temporal correlation and transition rules of user access in a smart city. In this paper, we analyze the characteristics of spatio-temporal data, and map the history of user access requests to the spatio-temporal attribute domain. Then, we perform correlation analysis and identify variation rules for access requests by using regional meshing, association rules and ARIMA in the spatio-temporal attribute domain, for the purpose of mining user access patterns and predict the user’s access request. Experimental results show that our pattern mining algorithms is simple yet effective, and it achieves a prediction accuracy of 84.3% for access requests.

previous article Research and application of biological potency soft sensor modeling method in the industrial fed-batch chlortetracycline fermentation process

next article Study on fault diagnosis algorithm in WSN nodes based on RPCA model and SVDD for multi-class classification

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Pallis, G., Vakali, A., Pokorny, J.: A clustering-based prefetching scheme on a web cache environment. Comput. Electr. Eng. 34(4), 309–323 (2008)CrossRef

Wan, M., Jönsson, A., Wang, C., et al.: Web user clustering and web prefetching using random indexing with weight functions. Knowl. Inf. Syst. 33(1), 89–115 (2011)CrossRef

Cadez, I., Heckerman, D., Meek, C., et al.: Model-based clustering and visualization of navigation patterns on a web site. Data Min. Knowl. Dis. 7(4), 399–424 (2003)MathSciNetCrossRef

Perkowitz, M., Etzioni, O.: Adaptive web sites: automatically synthesizing web pages. In: Proceedings of the American Association for Artificial Intelligence (AAAI-98), pp. 727–732 (1998)

Perkowitz, M., Etzioni, O.: Adaptive web sites. Commun. ACM 43(10), 152–158 (2000)CrossRef

Mobasher, B., Dai, H., Luo, T., et al.: Effective personalization based on association rule discovery from web usage data. In: Proceedings of International Workshop on Web Information & Data Management, pp. 9–15 (2001)

Matthews, S.G., Gongora, M.A., Hopgood, A.A., et al.: Temporal fuzzy association rule mining with 2-tuple linguistic representation. In: IEEE International Conference on Fuzzy Systems, pp. 1–8 (2012)

Matthews, S.G., Gongora, M.A., Hopgood, A.A., et al.: Web usage mining with evolutionary extraction of temporal fuzzy association rules. Knowl. Based Syst. 54(4), 66–72 (2013)CrossRef

Khosravi, M., Tarokh, M.J.: Dynamic mining of users interest navigation patterns using naive Bayesian method. In: IEEE International Conference on Intelligent Computer Communication and Processing, pp. 119–122 (2010)

10.

Selva Prabhu, A.P., Ravi, T.: Hilbert space clustering based chronological backward search for effective web sequential pattern mining. Int. J. Comput. Appl. 175(7), 43–52 (2017)

11.

Yang, J., Huang, H., Jin, X.: Mining web access sequence with improved apriori algorithm. In: IEEE International Conference on Computational Science and Engineering. IEEE (2017)

12.

Jalali, M., Mustapha, N., Mamat, A., et al.: Web user navigation pattern mining approach based on graph partitioning algorithm. J. Theor. Appl. Inf. Technol. 11, 1125–1130 (2008)

13.

Park, S., Suresh, N.C., Jeong, B.K.: Sequence-based clustering for Web usage mining: a new experimental framework and ANN-enhanced K-means algorithm. Data Knowl. Eng. 65(3), 512–543 (2008)CrossRef

14.

Mobasher, B.: Data mining for web personalization. In: The Adaptive Web, pp. 90–135. Springer, Berlin (2007)

15.

Joshi, A., Krishnapuram, R.: On mining web access logs. In: ACM SIGMOD Workshop on Research Issues in Data Mining & Knowledge Discovery, pp. 63–69 (2000)

16.

Shrivastava, M.V., Gupta, M.N.: Performance improvement of web usage mining by using learning based K-mean clustering. Int. J. Comput. Sci. Appl. ISSN: 2250–3765 (2012)

17.

Wang, T.Z.: The development of web log mining based on improve K-means clustering analysis. In: Advances in Computer Science and Information Engineering, pp. 613–618. Springer, Berlin (2012)

18.

Zhang, D., Lee, K., Lee, I.: Periodic pattern mining for spatio-temporal trajectories: a survey. In: Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering, pp. 306–313. IEEE (2016)

19.

Yu, Y.-W., Qi, J.-P., Lu, Y.-H., et al.: Distributed swarm pattern mining algorithm in big spatio-temporal trajectory data. Comput. Eng. Sci. 38(2), 255–261 (2016)

20.

Feng, Z., Zhu, Y.: A survey on trajectory data mining: techniques and applications. IEEE Access 4, 2056–2067 (2017)CrossRef

21.

Al-Serafi, A., Elragal, A.: Visual trajectory pattern mining: an exploratory study in baggage handling systems. In: Industrial Conference on Data Mining, pp. 159–173. Springer, Berlin (2014)CrossRef

22.

Choi, D.W., Pei, J., Heinis, T.: Efficient mining of regional movement patterns in semantic trajectories. Proc. Vldb Endow. 10(13), 2073–2084 (2017)CrossRef

23.

Ramos, J., César, A., Neves, J., et al.: Adapting the user path through trajectory data mining. In: The 8th International Symposium on Ambient Intelligence, Porto, Portugal, pp. 195–202 (2017)

24.

Yang, G., Huang, Z., Wang, X.: Comparison study of sub-trajectory clustering in data mining. IOP Conf. Ser.: Earth Environ. Sci. 69(1), 012143 (2017)CrossRef

25.

Debnath, M., Tripathi, P.K., Elmasri, R.: A novel approach to trajectory analysis using string matching and clustering. In: IEEE, International Conference on Data Mining Workshops, pp. 986–993. IEEE (2013)

26.

Yang, Y., Cui, Z., Wu, J., et al.: Trajectory analysis using spectral clustering and sequene pattern mining. J. Comput. Inf. Syst. 8(6), 2637–2645 (2012)

27.

Møgelmose, A., Trivedi, M.M., Moeslund, T.B.: Trajectory analysis and prediction for improved pedestrian safety: integrated framework and evaluations. In: Intelligent Vehicles Symposium, pp. 330–335. IEEE (2015)

28.

Silva, T.C.D, Zeitouni, K., Macedo, J., et al.: On-line mobility pattern discovering using trajectory data. In: International Conference on Extending Database Technology (2016)

29.

Li, F., Long, X., Du, S., et al.: Analyzing campus mobility patterns of college students by using GPS trajectory data and graph-based approach. In: International Conference on Geoinformatics, pp. 1–5. IEEE (2016)

30.

Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques: Concepts and Techniques. Elsevier, Amsterdam (2011)MATH

31.

Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1), 159–175 (2003)CrossRef

32.

Tran, N., Reed, D.: Automatic ARIMA time series modeling for adaptive I/O prefetching. IEEE Trans. Parallel Distrib. Syst. 15(4), 362–377 (2004)CrossRef

Title: Access patterns mining from massive spatio-temporal data in a smart city
Authors: Lian Xiong
Xiaojun Liu
Daixin Guo
Zhihua Hu
Publication date: 27-01-2018
Publisher: Springer US
Published in: Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-1791-1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Special Issue 3/2019

The performance simulation analysis of material tribological behavior based on the machine sensing algorithm

A critical study on the use of artificial intelligence, e-Learning technology and tools to enhance the learners experience

The data clustering based dynamic risk identification of biological immune system: mechanism, method and simulation

Jaw fracture classification using meta heuristic firefly algorithm with multi-layered associative neural networks

Attribute weights-based clustering centres algorithm for initialising K-modes clustering

Survey of access control models and technologies for cloud computing

Premium Partner