CHAPTER 5 RESULT ANALYSIS
5.2 Analysis of Rule Sets - PrefixSpan with Time Constraint
obtained therefrom. However, we discover an interesting trend that many users switched from Plurk to WhatsApp Messenger. This reflects the fact that users gradually turned to use other applications nowadays.
Table XVII. A portion of rules that starts with Plurk.
Plurk Plurk SUP:0.014
Plurk Plurk Plurk SUP:0.006
Plurk Plurk Browser SUP:0.006
Plurk WhatsApp Messenger SUP:0.01
Plurk WhatsApp Messenger WhatsApp Messenger SUP:0.006
Plurk Facebook SUP:0.01
5.2 Analysis of Rule Sets - PrefixSpan with Time Constraint
With the time constraint, we can find stronger time-relevant patterns. For example, we can find applications used within five seconds, shown in Table XVIII. For example, users using Camera then using Album; and users using htcdialer or htccontacts then using Phone. It comes from the fact that, not only these actions (taking photo, dialing number, opening contacts) can be done quickly, but also the actions that follow are considered highly correlated with the prior actions. Moreover, by Table XVIII, we also can see that when user wants make a phone call, he or she may choose to key in the number directly, instead of using the contacts list. This phenomenon can be provided to the smartphone designers, too. The designers can think about how to strengthen the connection between contacts and phone. Otherwise even
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number of recipients to make a phone call.Table XVIII. Sample rules with time constraint set to five seconds.5
5 Sec 30 Sec 60 Sec 180 Sec
Camera Album 153 306 427 292
htcdialer Phone 1651 2381 2440 2489
htccontacts Phone 193 389 441 510
When using communication applications such as WhatsApp Messenger and Gtalk, users usually spend 30 to 60 seconds before switching to another application. It is worth mentioning that we can find length-4 pattern consists of all WhatsApp Messenger with time constraint. It reflects users’ high retention on this application. On the other hand, social applications such as Facebook and Plurk take more time to finish. Users stay at this kind of applications for several minutes before they switch to the next application.
Table XIX. Sample rules related to communication and social applications.
5 Sec 30 Sec 60 Sec 180 Sec
WhatsApp Messenger Album - - 137 161
WhatsApp Messenger WhatsApp Messenger - 215 401 698
WhatsApp Messenger Facebook - - 165 264
5 From Table XVIII to Table XXI, the statistics corresponding to 5, 30, 60, and 180 seconds represent the numerators over the total number of sessions (25880).
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Unexpectedly, mailing tools such as Mail or Gmail are not used for so long. After about one minute, users will likely begin their next usage. Obviously, user use mailing tools in smartphones for receiving and browsing e-mails mostly, or reply briefly at best.
Table XX. Sample rules related to the Gmail application.
5 Sec 30 Sec 60 Sec 180 Sec
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Google Maps they tend to keep using for minutes. This may be explained by the scenario that users would stare at the maps for directions while walking and finding their points of interests, and thus the long usage period of Google Maps. This finding contradicts the common perception that users may switch between browser or communication applications for address information or directions while using Google Maps.
Table XXI. Sample rules related to the Google Maps application.
5 Sec 30 Sec 60 Sec 180 Sec
Google Maps Google Maps - - - 182
Google Maps Facebook - - - 136
Google Maps QCustomShortcut - - - 161
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CHAPTER 6
CONCLUSIONS AND FUTURE WORK
In this thesis, we presented our study on the analysis of a data set that contains log records generated by several smartphone users during a long period of time. We discussed in detail the problem modeling and gave practical information after dealing with the raw data.
Most of all, we used data mining methods to analyze user behaviors on smartphones, and additionally we gave some real examples.
The contribution of the thesis is to introduce the process of smartphone users’ log mining and discover hidden information from large amount of smartphone users’ log data collected by the platform automatically and without strong assumptions. Three implementations are proposed including association rule mining, sequential pattern mining, and sequential pattern mining with time constraint. We show the frequent patterns from the data mining tasks and present useful and interesting analysis about users’ general navigation behavior; we also can reconstruct and restore the users’ usage scenario when using smartphone.
We provided rules and patterns that are extracted from a real data set and could be beneficial to the designers of smartphone applications or user interfaces. Based on our results, the designers of camera applications can consider improving their functions for better browsing, viewing, and editing photos as well as for better integrating with social web sites.
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Also, social applications should add more features of photo-taking and photo-editing to enhance its photo-sharing experience. To sum up, the rule sets could help the designers develop better applications, such as “one-stop” applications to the users, by which users could enjoy more functions without having to switch between applications frequently.
However, there are limitations in this thesis due to the nature of the raw data. First, the data was collected during a six-month period, between September 2010 and March 2011.
Though it is enough to do some research topics such as our research issue that analyze the applications user used, it is not enough for doing some research topics such as analyzing the specific user’s particular usage pattern. In addition, the quality of geographic information is not accurate and complete, since the error range did exist in not only 3G systems but also Wi-Fi system. Based on the most realistic way of collecting data, we do not interfere with users when they open the network, nor do we specify which type of positioning systems users choose.
As for our future work, we would like to perform more analyses on the log records to have findings even more beneficial to the designers. We plan to investigate better data mining methods (extended from the existing ones) in order to extract more informative and human-readable rules. On other issues, we can analyze this data based on the user’s own particular usage pattern such as finding the user’s usage pattern from being a new user to the smartphone, to the skilled user and observing his or her usage shift. We also can zoom in or out the granularity of research viewpoint, for macroscopic viewpoint, we can aggregate applications to groups or group labels, and analyze the relationships among groups; for microcosmic viewpoint, we can analyze the behavior of the application.
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Appendix I. Rules generated from PrefixSpan. Min-Sup=0.01.
1 Facebook Plurk SUP: 0.010046
17 QCustomShortcut Packageinstaller SUP: 0.014219 18 QCustomShortcut Privacy Blocker SUP: 0.015997
19 QCustomShortcut Facebook SUP: 0.017504
20 QCustomShortcut QCustomShortcut SUP: 0.037674
21 QCustomShortcut Browser SUP: 0.024575
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47 WhatsApp Messenger WhatsApp Messenger SUP: 0.03755848 WhatsApp Messenger Facebook SUP: 0.014606
49 WhatsApp Messenger Gmail SUP: 0.015533
50 WhatsApp Messenger android SUP: 0.011669
51 Privacy Blocker Privacy Blocker SUP: 0.015881 52 Privacy Blocker QCustomShortcut SUP: 0.015417
53 Privacy Blocker Browser SUP: 0.010549
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69 htcdialer QCustomShortcut SUP: 0.011476
70 htcdialer Gmail SUP: 0.01051
76 Packageinstaller QCustomShortcut SUP: 0.011592
77 Album Album SUP: 0.011978
78 Album Camera SUP: 0.010278
79 Mail Gmail SUP: 0.010317
80 LauncherPro com.google.android.gsf SUP: 0.010549
81 LauncherPro LauncherPro SUP: 0.012751
93 QCustomShortcut QCustomShortcut QCustomShortcut SUP: 0.017465 94 QCustomShortcut Browser QCustomShortcut SUP: 0.011283
95 QCustomShortcut Browser Browser SUP: 0.010394
96 Phone htcdialer htcdialer SUP: 0.013872
97 Phone htcdialer Phone SUP: 0.033462
98 Phone htccontacts Phone SUP: 0.013872
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101 WhatsApp Messenger WhatsApp Messenger WhatsApp Messenger SUP: 0.021484
102 Gtalk Gtalk Gtalk SUP: 0.012094
113 QCustomShortcut QCustomShortcut QCustomShortcut QCustomShortcut SUP: 0.010317
114 Phone htcdialer htcdialer Phone SUP: 0.011901
Messenger SUP: 0.014297
119 htcdialer htcdialer Phone Phone SUP: 0.010355
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Appendix II. Rules generated from PrefixSpan
with time constraints. Min-Sup=0.005.
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131 QCustomShortcut Packageinstaller QCustomShortcut - - 155 195 132 QCustomShortcut QCustomShortcut Packageinstaller - - - 158 133 QCustomShortcut QCustomShortcut QCustomShortcut - - 150 285 134 QCustomShortcut Browser QCustomShortcut - - - 145143 WhatsApp Messenger WhatsApp Messenger WhatsApp Messenger - - - 330
144 Gtalk Gtalk Gtalk - - - 149