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題名 智慧型手機使用模式之探勘
Mining Application Usage Patterns of Smartphone Users作者 曾菀柔
Tseng, Woan Rou貢獻者 徐國偉
Hsu, Kuo Wei
曾菀柔
Tseng, Woan Rou關鍵詞 資料探勘
智慧型手機
使用模式
Data mining
Sequential pattern mining
Smartphone application
User behavior日期 2012 上傳時間 2-九月-2013 16:48:27 (UTC+8) 摘要 近幾年內，行動裝置科技、尤其是智慧型手機的蓬勃發展，在許多方面都影響了人們工作和生活的方式。具備不同功能的應用程式，能夠增強智慧型手機的功能，並且更加符合使用者的個人需求，為人們帶來許多便利和娛樂。因此，了解使用者使用何種應用程式、以及使用者如何使用這些應用程式，是相當重要的議題。研究者們已經提出了許多相關的研究，以期提供更好的使用者經驗給智慧型手機使用者。然而，這類研究大多是根據問卷調查、或者在實驗室環境下所進行的實驗，只能有限的反映真實世界中的使用者行為。在本論文中，為了克服這些限制，實作了資料探勘的技術，應用在一長時間收集而來的真實資料上，以發掘使用者行為中的顯著規律。首先，本論文應用了傳統的關聯式規則探勘、以及序列式規則探勘的演算法，並且針對他們的不足進行討論。接著，本論文定義一段session為使用者單次的使用區段，包含使用者在這段期間內的行為記錄，並且提出一個資料前處理的流程以轉換原始資料成session。最後，本論文提出一個序列式規則探勘演算法的延伸，藉由限制session內應用程式使用的時間間隔，來產生代表更強烈關聯性的規則，並且蘊含更豐富的智慧型手機使用者行為資訊。本論文對於智慧型手機應用程式的使用者行為分析，做出以下三個貢獻：第一，本論文提供一套資料前處理的流程，可用於此類型的分析研究；第二，本論文提出一個從序列式規則探勘演算法所延伸而出的演算法，特別適用於智慧型手機使用者行為記錄資料的規則探勘；第三，本論文提供了規則的相關分析。本論文使用真實世界的資料集來探勘，發掘出的規則可以反映和解釋智慧型手機使用者的行為，並且，透過詳細的分析，這些結果有助於智慧型手機中，相關應用程式以及使用者介面的設計。
The development of smartphone technology has changed the way people work and live in many aspects in just a few years. The advanced operating systems on smartphones allow developers to create applications that enhance the functions of the smartphones, and the applications help users personalize their smartphones to enrich their work and lives. Various types of applications designed for smartphones emerge and bring people convenience and entertainment. Therefore, it is practically interesting and important to study what applications people use on their smartphones and how people use these applications. Many studies are conducted in order to provide a better user experience to smartphone users. However, most of these studies are based on questionnaire surveys or designed experiments that are performed in a laboratory setting and would be limited to reflect the actual application usage done by users in their daily lives. To overcome the limitation, we apply data mining techniques to a data set, which contains smartphone logs collected from several users over a long period of time, and intend to discover meaningful patterns that represent user behaviors. First of all, we apply the classic association rule mining algorithm and the traditional sequential pattern mining algorithm to the data set, and we discuss their weaknesses for mining application usage patterns of smartphone users. Then, we propose a data processing process that transforms the logs into sessions rather than transactions or sequences. We define a session as a user’s one-time usage period. Each session contains the user behavior during the period, including the applications used. Next, we propose an extension of a sequential pattern mining that allows us to take time constraints into account. Stronger rules can be generated by restricting shorter intervals between applications used, and they give us much deeper knowledge of user behaviors. In summary, this thesis contributes to user behavior analysis for smartphone applications from the following three aspects: First, it provides a data processing process that is essential for the analysis. Second, it provides an algorithm extended from a widely used sequential pattern mining algorithm and designed to discover rules from logs recording activities of smartphone users. Third, it provides an analysis on the discovered rules. This thesis contributes to a better understanding of smartphone users’ behaviors and a detailed analysis to the results. It uses a real data set to discover rules that could be beneficial to the designers of smartphone applications and user interfaces.參考文獻 [1] H. Verkasalo. “Analysis of Smartphone User Behavior,” in Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR), 2010 Ninth International Conference on. IEEE, June 2010, pp. 258-263.[2] P. Lei, T. Shen, W. Peng, and I. Su. “Exploring Spatial-Temporal Trajectory Model for Location Prediction,” 12th IEEE International Conference on Mobile Data Management, 2011.[3] C. Hung, C. Chang, and W. Peng. “Mining Trajectory Profiles for Discovering User Communities,” ACM LBSN’09, Seattle, WA, USA, November 2009.[4] C. Hung, W. Peng, and W. Lee. “Clustering and Aggregating clues for trajectories for mining trajetory patterns and routes,” The VLDB Journal, November 2011.[5] L. Wei, W. Peng, B. Chen, and T. Lin. “PATS: A Framework of Pattern-Aware Trajectory Search,” 11th IEEE International Conference on Mobile Data Management, 2010.[6] G. Chittaranjan, J. Blom, and D. Gatica-Perez. “Mining large-scale smartphone data for personality studies,” IEEE International Symposium on Wearable Computers, San Francisco, CA, June 2011.[7] L. Xie, X. Zhang, J. Seifert, and S. Zhu. “pBMDS: A Behavior-based Malware Detection System for Cellphone Devices,” ACM WiSec’10, Hoboken, NJ, March 2010.[8] I. Burguera, U. Zurutuza, and S. Nadjm-Tehrani. “Crowdroid: Behavior-Based Malware Detection System for Android,” ACM SPSM’11, Chicago, IL, October 2011.[9] O. Franko, and T. Tirrell. “Smartphone App Use Among Medical Providers in ACGME Training Programs,” Journal of Medical Systems, Volume 36 Issue 5, October 2012, pp. 3135-3139.[10] T. Smura. “Access alternatives to mobile services and content: analysis of handset-based smartphone usage data,” ITS 17th Biennial Conference, Montreal, Canada, June, 2008.[11] H. Verkasalo. “Analysis of Smartphone User Behavior,” 2010 Ninth International Conference on Mobile Business / 2010 Ninth Global Mobility Roundtable, Athens, Greece, June, 2010.[12] Q. Xu, J. Erman, A. Gerber, Z. Mao, J. Pang, and S. Venkataraman. “Identifying diverse usage behaviors of smartphone apps,” IMC’11 Proceedings of the 2011 ACM SIGCOMM Conference on Internet measurement conference, New York, USA, 2011, pp. 329-344.[13] H. Falaki, R. Mahajan, S. Kandula, D. Lymberopoulos, R. Govindan, and D. Estrin. “Diversity in Smartphone Usage,” ACM MobiSys’10, June 2010, San Francisco, CA, June 2010.[14] J. Kang, S. Seo, and J. Hong. “Usage Pattern Analysis of Smartphones,” Network Operations and Management Symposium (APNOMS), 2011 13th Asia-Pacific, Taipei, Taiwan, September 2011.[15] M. Chen, J. Han, and P. S. Yu. “Data Mining: An Overview from a Database Perspective,” IEEE Transaction on Knowledge and Data Engineering, Vol. 8, No. 6, December 1996.[16] R. Agrawal, T. Imielinski, and A. Swami. “Mining Association Rules between Sets of Items in Large Databases,” ACM SIGMOD, Washington DC, USA, May 1993.[17] R. Agrawal, and R. Srikant. “Fast Algorithms for Mining Association Rules,” in Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), San Francisco, CA, USA, 1994.[18] R. Agrawal, and R. Srikant. “Mining Sequential Patterns,” in Proceedings of the 11th International Conference on Data Engineering (ICDE’95), 1995.[19] R, Iváncsy, and I. Vajk. “Frequent Pattern Mining in Web Log Data,” Journal of Applied Sciencces at Budapest Tech Hungary, Volume 3 Issue 1, 2006.[20] N. Mabroukeh, and C. Ezeife. “A Taxonomy of Sequential Pattern Mining Algorithms,” Journal of ACM Computing Surveys (CSUR), Volume 43 Issue 1, November 2010.[21] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U.Dayal, and M. Hsu. “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,” in Proceedings of the 2001 International Conference on Data Engineering (ICDE’01), Heidelberg, Germany, April 2001.[22] T. Rincy. N, and Y. Pandey. “Perfermance Evaluation on State of the Art Sequential Pattern Mining Algorithms,” International Journal of Computer Applications, Volume 65 Number 14, 2013.[23] J. Chen. “An UpDown Directed Acyclic Graph Approach for Sequential Pattern Mining,” IEEE Transactions on Knowledge and Data Engineering, Volume 22 Issue 7, July 2010.[24] P. Chen, C. Chen, W. Liao, and T. Li. “A Service Platform for Logging and Analyzing Mobile User Behaviors,” in Proceedings of Edutainment 2011, LNCS 6872, 2011.[25] P. Chen, H. Wu, C. Hsu, W. Liao, and T. Li. “Logging and Analyzing Mobile User Behaviors,” International Symposium on Cyber Behavior, Taipei, Taiwan, February 2012.[26] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. “The WEKA Data Mining Software: An Update,” SIGKDD Explorations, Volume 11 Issue 1, 2009.[27] Viger Philippe Fournier. SPMF- A Sequential Pattern Mining Framework, http://www.philippe-fournier-viger.com/spmg/[28] Q. Zhao, and S. S. Bhowmick. “Association Rule Mining: A Survey,” Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003116, 2003.[29] J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2011.[30] Q. Zhao, and S. S. Bhowmick. “Sequential Pattern Mining: A Survey,” Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003118, 2003. 描述 碩士
國立政治大學
資訊科學學系
100753016
101資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100753016 資料類型 thesis dc.contributor.advisor 徐國偉 zh_TW dc.contributor.advisor Hsu, Kuo Wei en_US dc.contributor.author (作者) 曾菀柔 zh_TW dc.contributor.author (作者) Tseng, Woan Rou en_US dc.creator (作者) 曾菀柔 zh_TW dc.creator (作者) Tseng, Woan Rou en_US dc.date (日期) 2012 en_US dc.date.accessioned 2-九月-2013 16:48:27 (UTC+8) - dc.date.available 2-九月-2013 16:48:27 (UTC+8) - dc.date.issued (上傳時間) 2-九月-2013 16:48:27 (UTC+8) - dc.identifier (其他 識別碼) G0100753016 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/59439 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 100753016 zh_TW dc.description (描述) 101 zh_TW dc.description.abstract (摘要) 近幾年內，行動裝置科技、尤其是智慧型手機的蓬勃發展，在許多方面都影響了人們工作和生活的方式。具備不同功能的應用程式，能夠增強智慧型手機的功能，並且更加符合使用者的個人需求，為人們帶來許多便利和娛樂。因此，了解使用者使用何種應用程式、以及使用者如何使用這些應用程式，是相當重要的議題。研究者們已經提出了許多相關的研究，以期提供更好的使用者經驗給智慧型手機使用者。然而，這類研究大多是根據問卷調查、或者在實驗室環境下所進行的實驗，只能有限的反映真實世界中的使用者行為。在本論文中，為了克服這些限制，實作了資料探勘的技術，應用在一長時間收集而來的真實資料上，以發掘使用者行為中的顯著規律。首先，本論文應用了傳統的關聯式規則探勘、以及序列式規則探勘的演算法，並且針對他們的不足進行討論。接著，本論文定義一段session為使用者單次的使用區段，包含使用者在這段期間內的行為記錄，並且提出一個資料前處理的流程以轉換原始資料成session。最後，本論文提出一個序列式規則探勘演算法的延伸，藉由限制session內應用程式使用的時間間隔，來產生代表更強烈關聯性的規則，並且蘊含更豐富的智慧型手機使用者行為資訊。本論文對於智慧型手機應用程式的使用者行為分析，做出以下三個貢獻：第一，本論文提供一套資料前處理的流程，可用於此類型的分析研究；第二，本論文提出一個從序列式規則探勘演算法所延伸而出的演算法，特別適用於智慧型手機使用者行為記錄資料的規則探勘；第三，本論文提供了規則的相關分析。本論文使用真實世界的資料集來探勘，發掘出的規則可以反映和解釋智慧型手機使用者的行為，並且，透過詳細的分析，這些結果有助於智慧型手機中，相關應用程式以及使用者介面的設計。 zh_TW dc.description.abstract (摘要) The development of smartphone technology has changed the way people work and live in many aspects in just a few years. The advanced operating systems on smartphones allow developers to create applications that enhance the functions of the smartphones, and the applications help users personalize their smartphones to enrich their work and lives. Various types of applications designed for smartphones emerge and bring people convenience and entertainment. Therefore, it is practically interesting and important to study what applications people use on their smartphones and how people use these applications. Many studies are conducted in order to provide a better user experience to smartphone users. However, most of these studies are based on questionnaire surveys or designed experiments that are performed in a laboratory setting and would be limited to reflect the actual application usage done by users in their daily lives. To overcome the limitation, we apply data mining techniques to a data set, which contains smartphone logs collected from several users over a long period of time, and intend to discover meaningful patterns that represent user behaviors. First of all, we apply the classic association rule mining algorithm and the traditional sequential pattern mining algorithm to the data set, and we discuss their weaknesses for mining application usage patterns of smartphone users. Then, we propose a data processing process that transforms the logs into sessions rather than transactions or sequences. We define a session as a user’s one-time usage period. Each session contains the user behavior during the period, including the applications used. Next, we propose an extension of a sequential pattern mining that allows us to take time constraints into account. Stronger rules can be generated by restricting shorter intervals between applications used, and they give us much deeper knowledge of user behaviors. In summary, this thesis contributes to user behavior analysis for smartphone applications from the following three aspects: First, it provides a data processing process that is essential for the analysis. Second, it provides an algorithm extended from a widely used sequential pattern mining algorithm and designed to discover rules from logs recording activities of smartphone users. Third, it provides an analysis on the discovered rules. This thesis contributes to a better understanding of smartphone users’ behaviors and a detailed analysis to the results. It uses a real data set to discover rules that could be beneficial to the designers of smartphone applications and user interfaces. en_US dc.description.tableofcontents CHAPTER 1 INTRODUCTION 11.1 Motivation 11.2 The Research Object 21.3 Contributions of the Thesis 31.4 Organization of the Thesis 4CHAPTER 2 BACKGROUND 52.1 Traditional Research Methods 52.2 Researches on Mobile Phone 62.2.1 Reality mining 62.2.2 Trajectory 72.3 Analysis of Smartphone User Behavior 82.4 Data Mining Methods 102.4.1 Association Rule Mining Problem 112.4.2 Apriori-based Approach 122.4.3 Sequential Pattern Mining Problem 122.4.4 PrefixSpan by Pattern-Growth Approach 14CHAPTER 3 DATA 183.1 Raw Data Description 183.2 Data Processing 193.3 Summary Statistics of Data 21CHAPTER 4 EXPERIMENT 294.1 Association Rule Mining Method 304.1.1 Data Processor 304.1.2 Association Rule Mining 314.1.3 Results 324.2 Sequential Pattern Mining Method 344.2.1 Data Preprocessoring 354.2.2 Sequential Pattern Mining 374.3 PrefixSpan Algorithm with Time Constraint 394.4 More Experiments 41CHAPTER 5 RESULT ANALYSIS 435.1 Analysis of Rule Sets - PrefixSpan 435.2 Analysis of Rule Sets - PrefixSpan with Time Constraint 46CHAPTER 6 CONCLUSIONS AND FUTURE WORK 50REFERENCES 52Appendix I. Rules generated from PrefixSpan. Min-Sup=0.01. 56Appendix II. Rules generated from PrefixSpan with time constraints. Min-Sup=0.005. 60 zh_TW dc.format.extent 1513792 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100753016 en_US dc.subject (關鍵詞) 資料探勘 zh_TW dc.subject (關鍵詞) 智慧型手機 zh_TW dc.subject (關鍵詞) 使用模式 zh_TW dc.subject (關鍵詞) Data mining en_US dc.subject (關鍵詞) Sequential pattern mining en_US dc.subject (關鍵詞) Smartphone application en_US dc.subject (關鍵詞) User behavior en_US dc.title (題名) 智慧型手機使用模式之探勘 zh_TW dc.title (題名) Mining Application Usage Patterns of Smartphone Users en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] H. Verkasalo. “Analysis of Smartphone User Behavior,” in Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR), 2010 Ninth International Conference on. IEEE, June 2010, pp. 258-263.[2] P. Lei, T. Shen, W. Peng, and I. Su. “Exploring Spatial-Temporal Trajectory Model for Location Prediction,” 12th IEEE International Conference on Mobile Data Management, 2011.[3] C. Hung, C. Chang, and W. Peng. “Mining Trajectory Profiles for Discovering User Communities,” ACM LBSN’09, Seattle, WA, USA, November 2009.[4] C. Hung, W. Peng, and W. Lee. “Clustering and Aggregating clues for trajectories for mining trajetory patterns and routes,” The VLDB Journal, November 2011.[5] L. Wei, W. Peng, B. Chen, and T. Lin. “PATS: A Framework of Pattern-Aware Trajectory Search,” 11th IEEE International Conference on Mobile Data Management, 2010.[6] G. Chittaranjan, J. Blom, and D. Gatica-Perez. “Mining large-scale smartphone data for personality studies,” IEEE International Symposium on Wearable Computers, San Francisco, CA, June 2011.[7] L. Xie, X. Zhang, J. Seifert, and S. Zhu. “pBMDS: A Behavior-based Malware Detection System for Cellphone Devices,” ACM WiSec’10, Hoboken, NJ, March 2010.[8] I. Burguera, U. Zurutuza, and S. Nadjm-Tehrani. “Crowdroid: Behavior-Based Malware Detection System for Android,” ACM SPSM’11, Chicago, IL, October 2011.[9] O. Franko, and T. Tirrell. “Smartphone App Use Among Medical Providers in ACGME Training Programs,” Journal of Medical Systems, Volume 36 Issue 5, October 2012, pp. 3135-3139.[10] T. Smura. “Access alternatives to mobile services and content: analysis of handset-based smartphone usage data,” ITS 17th Biennial Conference, Montreal, Canada, June, 2008.[11] H. Verkasalo. “Analysis of Smartphone User Behavior,” 2010 Ninth International Conference on Mobile Business / 2010 Ninth Global Mobility Roundtable, Athens, Greece, June, 2010.[12] Q. Xu, J. Erman, A. Gerber, Z. Mao, J. Pang, and S. Venkataraman. “Identifying diverse usage behaviors of smartphone apps,” IMC’11 Proceedings of the 2011 ACM SIGCOMM Conference on Internet measurement conference, New York, USA, 2011, pp. 329-344.[13] H. Falaki, R. Mahajan, S. Kandula, D. Lymberopoulos, R. Govindan, and D. Estrin. “Diversity in Smartphone Usage,” ACM MobiSys’10, June 2010, San Francisco, CA, June 2010.[14] J. Kang, S. Seo, and J. Hong. “Usage Pattern Analysis of Smartphones,” Network Operations and Management Symposium (APNOMS), 2011 13th Asia-Pacific, Taipei, Taiwan, September 2011.[15] M. Chen, J. Han, and P. S. Yu. “Data Mining: An Overview from a Database Perspective,” IEEE Transaction on Knowledge and Data Engineering, Vol. 8, No. 6, December 1996.[16] R. Agrawal, T. Imielinski, and A. Swami. “Mining Association Rules between Sets of Items in Large Databases,” ACM SIGMOD, Washington DC, USA, May 1993.[17] R. Agrawal, and R. Srikant. “Fast Algorithms for Mining Association Rules,” in Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), San Francisco, CA, USA, 1994.[18] R. Agrawal, and R. Srikant. “Mining Sequential Patterns,” in Proceedings of the 11th International Conference on Data Engineering (ICDE’95), 1995.[19] R, Iváncsy, and I. Vajk. “Frequent Pattern Mining in Web Log Data,” Journal of Applied Sciencces at Budapest Tech Hungary, Volume 3 Issue 1, 2006.[20] N. Mabroukeh, and C. Ezeife. “A Taxonomy of Sequential Pattern Mining Algorithms,” Journal of ACM Computing Surveys (CSUR), Volume 43 Issue 1, November 2010.[21] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U.Dayal, and M. Hsu. “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,” in Proceedings of the 2001 International Conference on Data Engineering (ICDE’01), Heidelberg, Germany, April 2001.[22] T. Rincy. N, and Y. Pandey. “Perfermance Evaluation on State of the Art Sequential Pattern Mining Algorithms,” International Journal of Computer Applications, Volume 65 Number 14, 2013.[23] J. Chen. “An UpDown Directed Acyclic Graph Approach for Sequential Pattern Mining,” IEEE Transactions on Knowledge and Data Engineering, Volume 22 Issue 7, July 2010.[24] P. Chen, C. Chen, W. Liao, and T. Li. “A Service Platform for Logging and Analyzing Mobile User Behaviors,” in Proceedings of Edutainment 2011, LNCS 6872, 2011.[25] P. Chen, H. Wu, C. Hsu, W. Liao, and T. Li. “Logging and Analyzing Mobile User Behaviors,” International Symposium on Cyber Behavior, Taipei, Taiwan, February 2012.[26] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. “The WEKA Data Mining Software: An Update,” SIGKDD Explorations, Volume 11 Issue 1, 2009.[27] Viger Philippe Fournier. SPMF- A Sequential Pattern Mining Framework, http://www.philippe-fournier-viger.com/spmg/[28] Q. Zhao, and S. S. Bhowmick. “Association Rule Mining: A Survey,” Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003116, 2003.[29] J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2011.[30] Q. Zhao, and S. S. Bhowmick. “Sequential Pattern Mining: A Survey,” Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003118, 2003. zh_TW
