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Presentation AppSensor at MobileHCI '11

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While applications for mobile devices have become extremely important in the last few years, little public information exists on mobile application usage behavior. We describe a large-scale......

While applications for mobile devices have become extremely important in the last few years, little public information exists on mobile application usage behavior. We describe a large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices. We present two types of results from analyzing this data: basic descriptive statistics and contextual descriptive statistics. In the case of the former, we find that the average session with an application lasts less than a minute, even though users spend almost an hour a day using their phones. Our contextual findings include those related to time of day and location. For instance, we show that news applications are most popular in the morning and games are at night, but communication applications dominate through most of the day. We also find that despite the variety of apps available, communication applications are almost always the first used upon a device's waking from sleep. In addition, we discuss the notion of a virtual application sensor, which we used to collect the data.

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  • 1. Falling Asleep with Facebook, Angry Birdsand Kindle — A Large-Scale Study onMobile Application UsageMatthias BöhmerBrent HechtJohannes SchöningAntonio KrügerGernot BauerMobileHCI 2011Stockholm, Sweden
  • 2. Mobile phones evolvedfrom communication devices...
  • 3. ... to devices supporting various tasks.
  • 4. Kray and Rohs, 2007; Satyanarayanan, 2005
  • 5. How do people use their apps?
  • 6. Related Work Girardello and Michahellis, 2009 McMillan et al., 2010 this paperNumber of users Henze et al., 2011 Duration Verkasalo, 2009 Demieux and Losquin, 2005 Froehlich et al., 2007 Number of apps
  • 7. # App Sensor
  • 8. being used open close not being used install uninstall updateApp Lifecycle
  • 9. being used open close not being usedApp Lifecycle
  • 10. Idea of App Sensor# Virtual sensor# Measuring app usage - When did user launch an app? - For how long did user use an app?
  • 11. Deployment of App Sensor# Implementation within appazaar* - Context-aware recommender system - Suggests apps to users - Uses App Sensor to inform its algorithms# 2Hz sampling rate# Collecting additional context information (location, time, and previously used apps)* you will find it on the Android Market
  • 12. Gathered data# 4,125 users from various countries# 22,626 apps from 20 categories (crawled from Market)# 4.92 million data points# 127 days
  • 13. # Basic Findings
  • 14. Usage time per launch (seconds) 50 100 150 200 250 300 unknown Finance Travel Comm. Product. Shopping Basic findings Social Sports News Settings Browser Entertain. # 59.23 minutes of app usage per day Multimedia Comics Games # 71.56 seconds per app launch on average Health Lifestyle Reference # Average app usage time differs between categories Tools ThemesLib. & Demos
  • 15. # Application Usage over Time
  • 16. App launches during course of the day12 am 1 am 2 am 3 am 4 am 5 am 6 am 7 am 8 am 9 am 10 am 11 am 12 pm 1 pm 2 pm 3 pm 4 pm 5 pm 6 pm 7 pm 8 pm 9 pm 10 pm 11 pm# App usage correlates with circadian circle# During the night usage time per launch is 6 times higher
  • 17. Probability of app launches per category ./ !"-$ !,-$ !!-$ !"#$ !,#$ !!#$ !-$ "-$ %-$ &-$ -$ (-$ )-$ *-$ +-$ !#$ "#$ %#$ &#$ #$ (#$ )#$ *#$ +#$ 5# ?=0@;:= )A+. )A). )A*. )A(. )A%. )A&. )A,. )A+. *A!. *A,. )A). )A%. )A,. (A+. (A*. (A&. (A(. (A(. (A&. (A(. )A,. )A&. )A. )A&. C0$D8; &A. A". A&. A*. A*. A(. A. A". A&. A!. &A). &A%. &A%. &A". &A". &A%. &A&. &A,. &A&. &A". &A!. &A!. &A!. &A&. C0$$67D8#3D07 &&A+. &!A!. %*A%. %A&. %!A(. %!A*. %"A). %&A). %+A&. &&A*. &+A,. "A(. &A*. A". A". (A!. A). (A*. )A!. (A!. &A*. %A%. "A,. &+A,. E73:=3#D7$:73 ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. ,A,. FD7#78: ,A". ,A%. ,A%. ,A". ,A!. ,A!. ,A!. ,A". ,A%. ,A%. ,A&. ,A. ,A%. ,A%. ,A&. ,A%. ,A%. ,A". ,A". ,A". ,A". ,A". ,A". ,A". G#$:; %A". %A,. %A,. "A). "A. "A%. "A". !A). !A+. !A+. "A,. "A!. "A". "A". "A". "A%. "A%. "A". "A". "A&. "A). %A,. %A,. %A". H:#439 ,A%. ,A&. ,A&. ,A&. ,A(. ,A(. ,A). ,A(. ,A&. ,A%. ,A%. ,A". ,A". ,A". ,A". ,A". ,A". ,A". ,A". ,A%. ,A". ,A%. ,A". ,A%.5DI=#=D:;/J/K:$0 ,A&. ,A. ,A(. ,A). ,A+. ,A*. ,A). ,A(. ,A. ,A&. ,A%. ,A%. ,A". ,A". ,A". ,A". ,A". ,A". ,A". ,A". ,A%. ,A%. ,A%. ,A%. 5D1:;3L4: ,A*. ,A+. !A,. !A&. !A%. !A. !A&. !A&. !A!. ,A+. ,A(. ,A(. ,A. ,A. ,A. ,A. ,A(. ,A. ,A%. ,A&. ,A&. ,A. ,A. ,A. M643D$:ND# "A!. "A!. "A&. "A&. "A). "A&. !A*. !A*. !A+. !A). !A*. "A,. "A,. "A,. "A". "A!. "A". "A&. "A%. "A%. "A". "A!. !A+. "A,. O:@; "A(. "A. "A(. "A. "A. "A). %A%. %A). &A!. %A(. %A,. "A(. "A. "A). "A. "A&. "A". "A!. "A%. "A". "A%. "A". "A%. "A%. P=0N683DQD3L %A(. A,. A,. A*. (A%. (A. (A,. A&. &A*. A!. &A+. &A%. &A". &A,. &A,. %A). %A&. %A&. %A,. %A!. %A!. %A,. "A+. %A". R:1:=:78: ,A). ,A). ,A). ,A). ,A). ,A). ,A(. ,A(. ,A). ,A. ,A. ,A. ,A&. ,A&. ,A&. ,A&. ,A%. ,A&. ,A&. ,A&. ,A. ,A. ,A. ,A(. S:33D7T; !A%. !A(. !A. !A%. !A(. !A". !A". !A!. !A%. !A&. !A&. !A&. !A". !A%. !A". !A". !A%. !A!. !A!. !A". !A". !A%. !A%. !A&. S90--D7T %A+. &A. %A). %A&. %A". %A". %A!. %A,. %A!. %A%. %A". %A". %A". "A*. "A+. "A+. "A). "A). "A). "A). "A*. %A!. %A(. %A. S08D#4 A). A,. &A+. &A%. &A". &A,. &A&. A!. A%. A&. A". A,. &A). &A*. &A+. &A. &A. &A(. &A(. &A+. A". A&. A*. A). S-0=3; ,A. ,A%. ,A%. ,A". ,A%. ,A%. ,A". ,A%. ,A%. ,A%. ,A%. ,A&. ,A&. ,A(. ,A). ,A*. ,A+. ,A*. ,A(. ,A(. ,A). ,A*. ,A). ,A). 29:$:; ,A". ,A!. ,A". ,A%. ,A&. ,A&. ,A&. ,A". ,A". ,A". ,A!. ,A!. ,A!. ,A". ,A!. ,A!. ,A!. ,A". ,A!. ,A!. ,A". ,A!. ,A!. ,A!. 2004; !,A+. !"A". !&A(. !)A(. ",A%. "!A. "!A&. !*A(. !&A). !,A&. *A&. (A*. (A!. A+. A+. A+. (A,. (A!. A*. (A,. (A%. (A*. )A&. +A!. 2=#Q:4 !A&. !A(. "A!. "A". "A&. "A(. "A". !A+. "A,. "A!. "A,. !A*. !A+. !A+. !A+. !A*. "A,. !A+. "A". "A". !A+. !A). !A(. !A&. <7U70@7 &A). A%. A!. A,. A%. &A&. A,. A+. &A(. &A&. &A!. %A*. %A. %A*. %A). %A). &A,. %A(. %A). %A). %A). %A+. &A!. &A. !,%B(,&/ !,+B,/ !")B,(+/ !&"B(&"/ !*B*)(/ !(*B,*"/ !(+B,!*/ !)"B+%/ !)%B+(%/ !)+B*,!/ !*&B,!"/ !)(B,,/ !(%B,*,/ !%B*%/ !&!B%,%/ !"%B(%+/ ))B,%/ %B(%%/ &,B%%"/ %%B&%*/ %,B+&+/ %*B!(!/ (B*+/ *%B&**/ 203#4/5#6789:;/ # Type of used apps changes during the course of the day -:=/H06=Figure 5.During day:app usage by category in terms of launches. Each cell value refers to the percentage of app launches d # Hourly relative primarily communication appswithin each hour for each category. Colors are normalized by row, with green indicating each category’s maximum percentage ofand white indicating each category’s minimum. For example,heterogenous in the evening (green) and trough in the morn # During night: scope of apps more games reach their peak
  • 18. # Chains of App Usage
  • 19. Categories of first apps in chains Tools 9.2 % Browser 7.2 % Social Communication 5.0 % 49.6 % Comics 4.9 % Productivity 4.3 % other 19.9 %# 73% of app chains only have one app
  • 20. Transitions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
  • 21. # Specific App Usage
  • 22. 12 am 1 am 2 am 3 am 4 am 5 am 6 am 7 am 8 am 9 am 10 am 11 am 12 pm Launches of Facebook 1 pm 2 pm 3 pm 4 pm 5 pm 6 pm 7 pm 8 pm 9 pm 10 pm# Using Facebook on mobile seems to be primarily an evening activity 11 pm Facebook (normalized) Basic population (normalized)
  • 23. 12 am 1 am 2 am 3 am 4 am 5 am 6 am 7 am 8 am 9 am 10 am 11 am 12 pm 1 pm Launches of Foursquare 2 pm# People seem to check-in when having a meal 3 pm 4 pm # Foursquare has peaks around lunch and dinner time 5 pm 6 pm 7 pm 8 pm 9 pm 10 pm 11 pm Foursquare (normalized) Basic population (normalized)
  • 24. 12 am 1 am 2 am 3 am 4 am 5 am 6 am 7 am 8 am 9 am 10 am Alarm 11 am 12 pm 1 pm 2 pm Launches of ??? Clock 3 pm 4 pm 5 pm 6 pm 7 pm 8 pm 9 pm 10 pm 11 pm# Some apps have spikes, others are used more broadly throughout the day Alarm Clock (normalized) Basic population (normalized)
  • 25. # Discussion
  • 26. Discussion# Menus should adapt to time, location, previously used apps# App design should incorporate apps‘ transition patterns# App Sensor can provide additional context information tourist using city guide shopper using shopping list
  • 27. # Conclusion
  • 28. Conclusion# App Sensor: a virtual sensor for measuring app usage# Findings based on public deployment - App usage is approx. 1h per day and 72sec per launch - App usage differs between categories - There is a functional cohesion between apps# Future Work - Better understand how context influences app usage - Access to anonymized data available for collaboration