Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2017, Article ID 3689309, 18 pages
https://doi.org/10.1155/2017/3689309
Research Article

Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage

Department of Computer Science, National Chengchi University, No. 64, Sec. 2, Zhi Nan Rd., Wen Shan District, Taipei 11605, Taiwan

Correspondence should be addressed to Kuo-Wei Hsu; wt.ude.uccn@ushwk

Received 9 August 2016; Revised 14 November 2016; Accepted 1 December 2016; Published 16 January 2017

Academic Editor: Sergio Mascetti

Copyright © 2017 Kuo-Wei Hsu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. B. Zhou, S. C. Hui, and K. Chang, “An intelligent recommender system using sequential web access patterns,” in Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, vol. 2, pp. 393–398, December 2004. View at Scopus
  2. N. R. Mabroukeh and C. I. Ezeife, “A taxonomy of sequential pattern mining algorithms,” ACM Computing Surveys, vol. 43, no. 1, article no. 3, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Srivastava, R. Cooley, M. Deshpande, and P. Tan, “Web usage mining: discovery and applications of usage patterns from Web data,” ACM SIGKDD Explorations Newsletter, vol. 1, no. 2, pp. 12–23, 2000. View at Publisher · View at Google Scholar
  4. J. Pei, J. Han, B. Mortazavi-Asl et al., “PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth,” in Proceedings of the 17th International Conference on Data Engineering, pp. 215–224, April 2001. View at Scopus
  5. J. Pei, J. Han, B. Mortazavi-Asl et al., “Mining sequential patterns by pattern-growth: the PrefixSpan approach,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 11, pp. 1424–1440, 2004. View at Publisher · View at Google Scholar
  6. P. Fournier-Viger, R. Nkambou, and E. M. Nguifo, “A knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems,” in Proceedings of the 7th Mexican International Conference on Artificial Intelligence, pp. 765–778, Atizapán de Zaragoza, Mexico, October 2008. View at Publisher · View at Google Scholar
  7. P. Fournier-Viger, A. Gomariz, T. Gueniche, A. Soltani, C.-W. Wu, and V. S. Tseng, “SPMF: a java open-source pattern mining library,” Journal of Machine Learning Research, vol. 15, article A28, pp. 3389–3393, 2014. View at Google Scholar · View at Scopus
  8. R. Agrawal and R. Srikant, “Mining sequential patterns,” in Proceedings of the IEEE 11th International Conference on Data Engineering, pp. 3–14, March 1995. View at Scopus
  9. Y. Hirate and H. Yamana, “Sequential pattern mining with time intervals,” in Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '06), pp. 775–779, Singapore, April 2006. View at Publisher · View at Google Scholar
  10. J. Pei, J. Han, and W. Wang, “Constraint-based sequential pattern mining: the pattern-growth methods,” Journal of Intelligent Information Systems, vol. 28, no. 2, pp. 133–160, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. N. Eagle and A. Pentland, “Reality mining: sensing complex social systems,” Personal and Ubiquitous Computing, vol. 10, no. 4, pp. 255–268, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Akoush and A. Sameh, “Mobile user movement prediction using Bayesian learning for neural networks,” in Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC '07), pp. 191–196, Honolulu, Hawaii, USA, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Wang, Y. Jia, and W. Han, “Instant message clustering based on extended vector space model,” in Advances in Computation and Intelligence: Second International Symposium, ISICA 2007 Wuhan, China, September 21–23, 2007 Proceedings, Lecture Notes in Computer Science, pp. 435–443, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  14. K. Farrahi and D. Gatica-Perez, “What did you do today? Discovering daily routines from large-scale mobile data,” in Proceedings of the 16th ACM International Conference on Multimedia (MM '08), pp. 849–852, ACM, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Farrahi and D. Gatica-Perez, “A probabilistic approach to mining mobile phone data sequences,” Personal and Ubiquitous Computing, vol. 18, no. 1, pp. 223–238, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Bhattacharya, L. Kulik, and J. Bailey, “Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections,” Pervasive and Mobile Computing, vol. 19, pp. 86–107, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Xie, X. Zhang, J.-P. Seifert, and S. Zhu, “PBMDS: a behavior-based malware detection system for cellphone devices,” in Proceedings of the 3rd ACM Conference on Wireless Network Security (WiSec '10), pp. 37–48, Hoboken, NJ, USA, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. I. Burguera, U. Zurutuza, and S. Nadjm-Tehrani, “Crowdroid: behavior-based malware detection system for android,” in Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices (SPSM '11), pp. 15–25, Chicago, Ill, USA, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Falaki, R. Mahajan, S. Kandula, D. Lymberopoulos, R. Govindan, and D. Estrin, “Diversity in smartphone usage,” in Proceedings of the 8th Annual International Conference on Mobile Systems, Applications and Services (MobiSys '10), pp. 179–194, ACM, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. J.-M. Kang, S.-S. Seo, and J. W.-K. Hong, “Usage pattern analysis of smartphones,” in Proceedings of the 13th Asia-Pacific Network Operations and Management Symposium, pp. 1–8, Taipei, Taiwan, September 2011. View at Publisher · View at Google Scholar
  21. Q. Xu, J. Erman, A. Gerber, Z. Mao, J. Pang, and S. Venkataraman, “Identifying diverse usage behaviors of smartphone apps,” in Proceedings of the ACM SIGCOMM Internet Measurement Conference (IMC '11), pp. 329–344, ACM, Berlin, Germany, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. J. K. Laurila, D. Gatica-Perez, I. Aad et al., “From big smartphone data to worldwide research: the mobile data challenge,” Pervasive and Mobile Computing, vol. 9, no. 6, pp. 752–771, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. Z. Wang, M. A. Nascimento, and M. H. MacGregor, “On the analysis of periodic mobility behavior,” in Proceedings of the Mobile Data Challenge Workshop in Conjunction with International Conference on Pervasive Computing, 2012.
  24. P. Montoliu, A. Martinez-Uso, J. Martinez-Sotoca, and J. McInerney, “Semantic place prediction by combining smart binary classifiers,” in Proceedings of the Mobile Data Challenge Workshop, in Conjunction with International Conference on Pervasive Computing, 2012.
  25. H. Gao, J. Tang, and H. Liu, “Mobile location prediction in spatio-temporal context,” in Proceedings of the Mobile Data Challenge Workshop, in conjunction with International Conference on Pervasive Computing, Newcastle, UK, June 2012.
  26. X. Wu, K. N. Brown, and C. J. Sreenan, “Analysis of smartphone user mobility traces for opportunistic data collection in wireless sensor networks,” Pervasive and Mobile Computing, vol. 9, no. 6, pp. 881–891, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. K. Huang, C. Zhang, X. Ma, and G. Chen, “Predicting mobile application usage using contextual information,” in Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp '12), pp. 1059–1065, Pittsburgh, Pa, USA, September 2012. View at Publisher · View at Google Scholar
  28. T. Yan, D. Chu, D. Ganesan, A. Kansal, and J. Liu, “Fast app launching for mobile devices using predictive user context,” in Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys '12), pp. 113–126, Lake District, UK, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. P. Dai, W. Yang, and S.-S. Ho, “Predicting mobile call behavior via subspace methods,” in Proceedings of the 6th International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction (SBP '13), pp. 466–475, Washington, DC, USA, April 2013. View at Publisher · View at Google Scholar
  30. E. H.-C. Lu, Y.-W. Lin, and J.-B. Ciou, “Mining mobile application sequential patterns for usage prediction,” in Proceedings of the IEEE International Conference on Granular Computing (GrC '14), pp. 185–190, IEEE, Noboribetsu, Japan, October 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Nanopoulos and Y. Manolopoulos, “Finding generalized path patterns for web log data mining,” in Current Issues in Databases and Information Systems, vol. 1884 of Lecture Notes in Computer Science, pp. 215–228, Springer, Berlin, Germany, 2000. View at Publisher · View at Google Scholar
  32. J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu, “Mining access patterns efficiently from web logs,” in Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '00), pp. 396–407, Kyoto, Japan, April 2000. View at Publisher · View at Google Scholar
  33. P.-M. Chen, C.-H. Chen, W.-H. Liao, and T.-Y. Li, “A service platform for logging and analyzing mobile user behaviors,” in Proceedings of the International Conference on E-Learning and Games, Edutainment, pp. 78–85, Taipei, Taiwan, September 2011. View at Publisher · View at Google Scholar
  34. P.-M. Chen, H.-Y. Wu, C.-Y. Hsu, W.-H. Liao, and T.-Y. Li, “Logging and analyzing mobile user behaviors,” in Proceedings of the International Symposium on Cyber Behavior (CB '12), Taipei, Taiwan, February 2012.
  35. W.-R. Tseng and K.-W. Hsu, “Mining sequential application usage patterns with time constraint of smartphone users,” in Proceedings of the International Conference on Cyber Behavior, Taipei, Taiwan, June 2014.
  36. W.-R. Tseng and K.-W. Hsu, “Smartphone app usage log mining,” International Journal of Computer and Electrical Engineering, vol. 6, no. 2, pp. 151–156, 2014. View at Publisher · View at Google Scholar
  37. F. Masseglia, P. Poncelet, and M. Teisseire, “Pre-processing time constraints for efficiently mining generalized sequential patterns,” in Proceedings of the 11th International Symposium on Temporal Representation and Reasoning (TIME '04), pp. 87–95, Normandie, France, July 2004. View at Scopus
  38. X. Yan, H. Cheng, J. Han, and D. Xin, “Summarizing itemset patterns: a profile-based approach,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 314–323, USA, August 2005. View at Publisher · View at Google Scholar · View at Scopus
  39. The JUNG Development Team, “JUNG: The Java Universal Network/Graph Framework,” 2006, http://jung.sourceforge.net.