Cited By
View all- Liu YMa YXiao XXie TLiu X(2023)LegoDroid: flexible Android app decomposition and instant installationScience China Information Sciences10.1007/s11432-021-3528-766:4Online publication date: 27-Mar-2023
Roaming Through the Castle Tunnels: An Empirical Analysis of Inter-app Navigation of Android Apps
Authors: 
Yun Ma, Ziniu Hu, Diandian Gu, Li Zhou, Qiaozhu Mei, Gang Huang, 
Xuanzhe LiuAuthors Info & Claims
Yun Ma, Ziniu Hu, Diandian Gu, Li Zhou, Qiaozhu Mei, Gang Huang, Xuanzhe LiuAuthors Info & ClaimsArticle No.: 14, Pages 1 - 24
Abstract
Smartphone applications (a.k.a., apps) have become indispensable in our everyday life and work. In practice, accomplishing a task on smartphones may require the user to navigate among various apps. Unlike Web pages that are inherently interconnected through hyperlinks, apps are usually isolated building blocks, and the lack of direct links between apps has compromised the efficiency of task completion and user experience. In this article, we present the first in-depth empirical study of page-level access behaviors of smartphone users based on a comprehensive dataset collected through an extensive user study. We propose a model to distinguish informational pages and transitional pages, based on which we can extract page-level inter-app navigation. Surprisingly, the transitional pages account for quite substantial time cost and manual actions when navigating from the current informational page to the desirable informational page. We reveal that developing “tunnels” between “isolated” apps under specific usage scenarios has a huge potential to reduce the cost of navigation. Our analysis provides some practical implications on how to improve app-navigation experience from both the operating system’s perspective and the developer’s<?brk?> perspective.
References
[1]
Marketing Land. 2015. Google App Streaming: A Big Move In Building “The Web Of Apps.” Retrieved from http://marketingland.com/google-app-streaming-web-of-apps-152449.
[2]
Android. 2019. Android Guide. Retrieved from http://developer.android.com/guide/components/index.html.
[3]
Microsoft. 2019. Bing App Linking. Retrieved from https://msdn.microsoft.com/en-us/library/dn614167.
[4]
Facebook. 2019. Facebook App Links. Retrieved from https://developers.facebook.com/docs/applinks.
[5]
Google. 2019. Google App Indexing. Retrieved from https://developers.google.com/app-indexing/.
[6]
Wikipedia. 2019. Mean reciprocal rank. Retrieved from https://en.wikipedia.org/wiki/Mean_reciprocal_rank.
[7]
Comscore. 2019. Mobile Internet Usage Skyrockets in Past 4 Years. Retrieved from http://www.comscore.com/Insights/Blog/Mobile-Internet-Usage-Skyrockets-in-Past-4-Years-to-Overtake-Desktop-as-Most-Used-Digital-Platform.
[8]
Wikipedia. 2019. Mobile deep linking. Retrieved from https://en.wikipedia.org/wiki/Mobile_deep_linking.
[9]
Corin R. Anderson, Pedro M. Domingos, and Daniel S. Weld. 2002. Relational Markov models and their application to adaptive web navigation. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’02). 143--152.
[10]
Tanzirul Azim, Oriana Riva, and Suman Nath. 2016. uLink: Enabling user-defined deep linking to app content. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys’16). 305--318.
[11]
Ricardo Baeza-Yates, Di Jiang, Fabrizio Silvestri, and Beverly Harrison. 2015. Predicting the next app that you are going to use. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM’15). 285--294.
[12]
Ravi Bhoraskar, Seungyeop Han, Jinseong Jeon, Tanzirul Azim, Shuo Chen, Jaeyeon Jung, Suman Nath, Rui Wang, and David Wetherall. 2014. Brahmastra: Driving apps to test the security of third-party components. In Proceedings of the 23rd USENIX Security Symposium. 1021--1036.
[13]
José Borges and Mark Levene. 1999. Data mining of user navigation patterns. In Proceedings of International Workshop on Web Usage Analysis and User Profiling (WEBKDD’99). 92--111.
[14]
Andrei Broder. 2002. A taxonomy of web search. In ACM SIGIR Forum, Vol. 36. 3--10.
[15]
Xinlei Chen, Yu Wang, Jiayou He, Shijia Pan, Yong Li, and Pei Zhang. 2019. CAP: Context-aware app usage prediction with heterogeneous graph embedding. Interact. Mobile Wear. Ubiq. Technol. 3, 1 (2019), 4:1–4:25.
[16]
Flavio Chierichetti, Ravi Kumar, Prabhakar Raghavan, and Tamás Sarlós. 2012. Are web users really Markovian? In Proceedings of the 21st World Wide Web Conference (WWW’12). 609--618.
[17]
Giuseppe Desolda, Carmelo Ardito, Hans-Christian Jetter, and Rosa Lanzilotti. 2019. Exploring spatially-aware cross-device interaction techniques for mobile collaborative sensemaking. Int. J. Hum.-Comput. Studies 122 (2019), 1–20.
[18]
Earlence Fernandes, Oriana Riva, and Suman Nath. 2016. Appstract: On-the-fly app content semantics with better privacy. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking (MobiCom’16). 361--374.
[19]
Denzil Ferreira, Jorge Goncalves, Vassilis Kostakos, Louise Barkhuus, and Anind K. Dey. 2014. Contextual experience sampling of mobile application micro-usage. In Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices 8 Services (MobileHCI’14). 91--100.
[20]
Xiaobin Fu, Jay Budzik, and Kristian J. Hammond. 2000. Mining navigation history for recommendation. In Proceedings of the 5th International Conference on Intelligent User Interfaces (IUI’00). 106--112.
[21]
B. Fuglede and F. Topsoe. 2004. Jensen-Shannon divergence and Hilbert space embedding. In Proceedings of the IEEE International Symposium on Information Theory (ISIT’04).
[22]
Lorenzo Gomez, Iulian Neamtiu, Tanzirul Azim, and Todd D. Millstein. 2013. RERAN: Timing- and touch-sensitive record and replay for Android. In Proceedings of the 35th International Conference on Software Engineering (ICSE’13). 72--81.
[23]
Aaron Halfaker, Oliver Keyes, Daniel Kluver, Jacob Thebault-Spieker, Tien T. Nguyen, Kenneth Shores, Anuradha Uduwage, and Morten Warncke-Wang. 2015. User session identification based on strong regularities in inter-activity time. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). 410--418.
[24]
Simon L. Jones, Denzil Ferreira, Simo Hosio, Jorge Goncalves, and Vassilis Kostakos. 2015. Revisitation analysis of smartphone app use. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). 1197--1208.
[25]
Taeyeon Ki, Alexander Simeonov, Bhavika Pravin Jain, Chang Min Park, Keshav Sharma, Karthik Dantu, Steven Y. Ko, and Lukasz Ziarek. 2017. Reptor: Enabling API virtualization on Android for platform openness. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys’17). 399–412.
[26]
Stephen G. Kobourov. 2012. Spring embedders and force directed graph drawing algorithms. CoRR abs/1201.3011 (2012). arXiv:1201.3011. http://arxiv.org/abs/1201.3011.
[27]
S. Kullback. 1987. The Kullback-Leibler distance. Amer. Stat. 41, 4 (1987), 340--341.
[28]
Huoran Li, Wei Ai, Xuanzhe Liu, Jian Tang, Feng Feng, Gang Huang, and Qiaozhu Mei. 2016. Voting with their feet: Inferring user preferences from app management activities. In Proceedings of the 25th International Conference on World Wide Web (WWW’16). 1351--1361.
[29]
Xiaoli Li and Bing Liu. 2003. Learning to classify texts using positive and unlabeled data. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI’03). 587--594.
[30]
Bing Liu, Yang Dai, X. Li, Wee Sun Lee, and P. S. Yu. 2003. Building text classifiers using positive and unlabeled examples. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM’03). 179--188.
[31]
Yuting Liu, Bin Gao, Tie-Yan Liu, Ying Zhang, Zhiming Ma, Shuyuan He, and Hang Li. 2008. BrowseRank: Letting web users vote for page importance. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’08). 451--458.
[32]
Xuan Lu, Wei Ai, Xuanzhe Liu, Qian Li, Ning Wang, Gang Huang, and Qiaozhu Mei. 2016. Learning from the ubiquitous language: An empirical analysis of emoji usage of smartphone users. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16). 770--780.
[33]
Yun Ma, Ziniu Hu, Yunxin Liu, Tao Xie, and Xuanzhe Liu. 2018. Aladdin: Automating release of deep-link APIs on Android. In Proceedings of the World Wide Web Conference (WWW’18). 1469--1478.
[34]
Akhil Mathur, Nicholas D. Lane, and Fahim Kawsar. 2016. Engagement-aware computing: Modelling user engagement from mobile contexts. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16). 622--633.
[35]
Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. 2001. On spectral clustering: Analysis and an algorithm. Proceedings of Conference on Neural Information Processing Systems (NIPS’01) 14 (2001), 849--856.
[36]
Damien Octeau, Somesh Jha, Matthew Dering, Patrick D. McDaniel, Alexandre Bartel, Li Li, Jacques Klein, and Yves Le Traon. 2016. Combining static analysis with probabilistic models to enable market-scale Android inter-component analysis. In Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL’16). 469--484.
[37]
Abhinav Parate, Matthias Hmer, David Chu, Deepak Ganesan, and Benjamin M. Marlin. 2013. Practical prediction and prefetch for faster access to applications on mobile phones. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’13). 275--284.
[38]
Ahmad Rahmati, Chad Tossell, Clayton Shepard, Philip Kortum, and Lin Zhong. 2012. Exploring iPhone usage: The influence of socioeconomic differences on smartphone adoption, usage and usability. In Proceedings of the 14th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI’12). 11--20.
[39]
Lenin Ravindranath, Jitendra Padhye, Sharad Agarwal, Ratul Mahajan, Ian Obermiller, and Shahin Shayandeh. 2012. AppInsight: Mobile app performance monitoring in the wild. In Proceedings of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI’12). 107--120.
[40]
Peter J. Rousseeuw. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 20 (1987), 53--65.
[41]
Clayton Shepard, Ahmad Rahmati, Chad Tossell, Lin Zhong, and Phillip Kortum. 2010. Livelab: Measuring wireless networks and smartphone users in the field. ACM Sigmetrics Perform. Eval. Rev. 38, 3 (2010), 15--20.
[42]
Vijay Srinivasan, Saeed Moghaddam, Abhishek Mukherji, Kiran K. Rachuri, Chenren Xu, and Emmanuel Munguia Tapia. 2014. MobileMiner: Mining your frequent patterns on your phone. In Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp’14). 389--400.
[43]
D. M. J. Tax. 2001. One-Class Classification. Doctoral Thesis. Delft Technical University.
[44]
Niels Van Berkel, Chu Luo, Theodoros Anagnostopoulos, Denzil Ferreira, Jorge Goncalves, Simo Hosio, and Vassilis Kostakos. 2016. A systematic assessment of smartphone usage gaps. In Proceedings of the Conference on Human Factors in Computing Systems (CHI’16). 4711--4721.
[45]
Feng Hsu Wang and Hsiu Mei Shao. 2004. Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert Syst. Appl. 27, 3 (2004), 365--377.
[46]
Huandong Wang, Yong Li, Sihan Zeng, Gang Wang, Pengyu Zhang, Pan Hui, and Depeng Jin. 2019. Modeling spatio-temporal app usage for a large user population. Interact. Mobile Wear. Ubiq. Technol. 3, 1 (2019), 27:1–27:23.
[47]
Robert West and Jure Leskovec. 2012. Human wayfinding in information networks. In Proceedings of the 21st World Wide Web Conference (WWW’12). 619--628.
[48]
Tingxin Yan, David Chu, Deepak Ganesan, Aman Kansal, and Jie Liu. 2012. 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). 113--126.
[49]
Sha Zhao, Zhiling Luo, Ziwen Jiang, Haiyan Wang, Feng Xu, Shijian Li, Jianwei Yin, and Gang Pan. 2019. AppUsage2Vec: Modeling smartphone app usage for prediction. In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE’19). 1322--1333.
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- Roaming Through the Castle Tunnels: An Empirical Analysis of Inter-app Navigation of Android Apps
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Publication History
Published: 27 June 2020
Online AM: 07 May 2020
Accepted: 01 April 2020
Revised: 01 January 2020
Received: 01 September 2018
Published in TWEB Volume 14, Issue 3
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- National Natural Science Foundation of China
- National Key R8D Program of China
- Key Laboratory of Intelligent Application Technology
- Beijing Outstanding Young Scientist Program
- National Science Foundation
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View all- Liu YMa YXiao XXie TLiu X(2023)LegoDroid: flexible Android app decomposition and instant installationScience China Information Sciences10.1007/s11432-021-3528-766:4Online publication date: 27-Mar-2023
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