Tortoise or Hare? Quantifying the Effects of Performance on Mobile App Retention
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Sasu Tarkoma | Eemil Lagerspetz | Pan Hui | Jukka Manner | Huber Flores | Petteri Nurmi | Agustin Zuniga | P. Hui | Sasu Tarkoma | Eemil Lagerspetz | Huber Flores | Agustin Zuniga | J. Manner | Petteri Nurmi
[1] Antti Oulasvirta,et al. Habits make smartphone use more pervasive , 2011, Personal and Ubiquitous Computing.
[2] Antti Jylhä,et al. How carat affects user behavior: implications for mobile battery awareness applications , 2014, CHI.
[3] Dilip Krishnaswamy,et al. MobInsight: On Improving The Performance of Mobile Apps in Cellular Networks , 2015, WWW.
[4] Steven Warburton,et al. Second Life in higher education: Assessing the potential for and the barriers to deploying virtual worlds in learning and teaching , 2009, Br. J. Educ. Technol..
[5] Sasu Tarkoma,et al. MobileCloudSim: A Context-aware Simulation Toolkit for Mobile Computational Offloading , 2018, UbiComp/ISWC Adjunct.
[6] Mahadev Satyanarayanan,et al. Using history to improve mobile application adaptation , 2000, Proceedings Third IEEE Workshop on Mobile Computing Systems and Applications.
[7] Sasu Tarkoma,et al. Exploiting Usage to Predict Instantaneous App Popularity , 2019, ACM Trans. Web.
[8] Jukka Riekki,et al. Modeling Mobile Code Acceleration in the Cloud , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).
[9] Paramvir Bahl,et al. Fine-grained power modeling for smartphones using system call tracing , 2011, EuroSys '11.
[10] Samir Ranjan Das,et al. Performance comparison of 3G and metro-scale WiFi for vehicular network access , 2010, IMC '10.
[11] Jorge Gonçalves,et al. Contextual experience sampling of mobile application micro-usage , 2014, MobileHCI '14.
[12] Selim Ickin,et al. Factors influencing quality of experience of commonly used mobile applications , 2012, IEEE Communications Magazine.
[13] Yvonne Rogers,et al. Why It's Worth the Hassle: The Value of In-Situ Studies When Designing Ubicomp , 2007, UbiComp.
[14] Sasu Tarkoma,et al. The hidden image of mobile apps: geographic, demographic, and cultural factors in mobile usage , 2018, MobileHCI.
[15] Teresa A. Myers. Goodbye, Listwise Deletion: Presenting Hot Deck Imputation as an Easy and Effective Tool for Handling Missing Data , 2011 .
[16] Roderick J A Little,et al. A Review of Hot Deck Imputation for Survey Non‐response , 2010, International statistical review = Revue internationale de statistique.
[17] Ricardo Baeza-Yates,et al. Predicting The Next App That You Are Going To Use , 2015, WSDM.
[18] Chin-Laung Lei,et al. How sensitive are online gamers to network quality? , 2006, CACM.
[19] Abhik Roychoudhury,et al. Detecting energy bugs and hotspots in mobile apps , 2014, SIGSOFT FSE.
[20] Feng Qian,et al. A close examination of performance and power characteristics of 4G LTE networks , 2012, MobiSys '12.
[21] Ning Chen,et al. AR-miner: mining informative reviews for developers from mobile app marketplace , 2014, ICSE.
[22] Samuel P. Midkiff,et al. What is keeping my phone awake?: characterizing and detecting no-sleep energy bugs in smartphone apps , 2012, MobiSys '12.
[23] Ahmed E. Hassan,et al. What Do Mobile App Users Complain About? , 2015, IEEE Software.
[24] Jukka Manner,et al. Netradar - Measuring the wireless world , 2013, 2013 11th International Symposium and Workshops on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt).
[25] Srinivasan Seshan,et al. Developing a predictive model of quality of experience for internet video , 2013, SIGCOMM.
[26] Vyas Sekar,et al. Understanding the impact of video quality on user engagement , 2011, SIGCOMM.
[27] Christos Faloutsos,et al. Why people hate your app: making sense of user feedback in a mobile app store , 2013, KDD.
[28] Kuan-Ta Chen,et al. On the battle between lag and online gamers , 2011, 2011 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR).
[29] Hari Balakrishnan,et al. WiFi, LTE, or Both?: Measuring Multi-Homed Wireless Internet Performance , 2014, Internet Measurement Conference.
[30] Sasu Tarkoma,et al. Carat: collaborative energy diagnosis for mobile devices , 2013, SenSys '13.
[31] Mohammad Salehan,et al. Social networking on smartphones: When mobile phones become addictive , 2013, Comput. Hum. Behav..
[32] Mahadev Satyanarayanan,et al. Multi-fidelity algorithms for interactive mobile applications , 1999, DIALM '99.
[33] Ahmad Rahmati,et al. Understanding human-battery interaction on mobile phones , 2007, Mobile HCI.
[34] Peter Brooks,et al. User measures of quality of experience: why being objective and quantitative is important , 2010, IEEE Network.
[35] Ratul Mahajan,et al. AppInsight: Mobile App Performance Monitoring in the Wild , 2022 .
[36] Shobha Venkataraman,et al. Prometheus: toward quality-of-experience estimation for mobile apps from passive network measurements , 2014, HotMobile.
[37] Mark de Reuver,et al. Smartphone Measurement: do People Use Mobile Applications as they Say they do? , 2012, ICMB.
[38] Sasu Tarkoma,et al. Constella: Crowdsourced system setting recommendations for mobile devices , 2016, Pervasive Mob. Comput..
[39] Sasu Tarkoma,et al. Energy modeling of system settings: A crowdsourced approach , 2015, 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[40] Rocky K. C. Chang,et al. Measuring the quality of experience of HTTP video streaming , 2011, 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops.
[41] Johannes Schöning,et al. Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage , 2011, Mobile HCI.
[42] Vassilis Kostakos,et al. Evidence-Aware Mobile Computational Offloading , 2018, IEEE Transactions on Mobile Computing.
[43] Marie Reilly,et al. Data analysis using hot deck multiple imputation , 1993 .