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Extract: Mining Social Features from WLAN Traces: A Gender-Based Case Study 

Extract: Mining Social Features from WLAN Traces: A Gender-Based Case Study . By Udayan Kumar Ahmed Helmy University of Florida. Presented by Ahmed Alghamdi. Outline. Introduction Motivations Challenges and Research Questions Contribution Approach

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Extract: Mining Social Features from WLAN Traces: A Gender-Based Case Study 

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  1. Extract: Mining Social Features from WLAN Traces: A Gender-Based Case Study  By UdayanKumar Ahmed Helmy University of Florida Presented by Ahmed Alghamdi

  2. Outline • Introduction • Motivations • Challenges and Research Questions • Contribution • Approach • Location Based Classification (LBC) • Group Behavior Based Filtering (GBF) • Hybrid filtering (HF) • Name Based Classification (NBC) • Validation of (LBC) • Temporal Consistency Validation • IBF vs. GBF • Cross Validation • User Behavior Analysis • User Spatial Distribution • Average Duration or Temporal Analysis • Device Preference • Application • Conclusion

  3. Introduction • WLAN traces to understand mobile user characteristics and behavior • Essential to network modeling and designing • This paper provide techniques to classify WLAN users into social groups • By area • By users’ info • it presents general methodology with an example case study of grouping by gender with investigation of gender gaps in WLAN usage

  4. Introduction • WLAN Traces • From 2 Universities (more than 50K users) • Over 3 Years • U1 - Feb 2006, Oct 2006, and Feb 2007 • U2 - Nov 2007, Apr 2008 • WLAN traces are logs of user association with a Wireless Access Point (AP) • Traces generally contain • machine’s MAC address • associating time • duration • associated AP • WLAN traces are fed into a database for easy SQL retrieving

  5. Motivations • Mobile devices becomes tightly coupled to users • Communication performance is bound to user mobility and behavior • In AdHoc networks, any node can act as a router • It is imperative to understand the various aspects of user behavior to design efficient protocols and effective network models

  6. Challenges and Research Questions • How can we meaningfully infer gender information from such anonymous traces? • Does gender information influence user behavior and preference in a significant and consistent manner? • what is the impact of these finding on network modeling, protocol and service design in the future?

  7. Contributions • Class and gender inference methods based on location, usage and name filtering from extensive WLAN traces • Providing the first gender-based trace-driven analysis in mobile societies, including study of majors and device preferences • Identifying unique features in the studied grouping that suggests consistent behavior and the design of potential future applications

  8. Approach • gender classification on campus • Location-based method • Based on individual and group network behavior • Analysis of WLAN traces • Cross validation with ground truth using Name based method • 90% Accuracy • Usage patterns of males and females are different • Gender does affect user activity and vendor preference • This contribution enhances the understanding of the mobile society • It is essential to provide efficient network protocols and services in the future

  9. Approach • Gender-Based Grouping • Location Based Classification (LBC) • Name Based Classification (NBC)

  10. Location Based Classification (LBC) • Sororities APs - female • Fraternities APs - males • CS Dept. APs - CS Students • Visitors Filtering • Visitor • Is a user with less number of sessions and smaller duration of sessions than the average user in that location(group behavior) • Or as user who has more sessions and larger online duration at other locations (individual behavior)

  11. Location Based Classification (LBC) • Individual Behavior Based filtering (IBF)  • The probability of a user being male or female by counting the number of sessions and measuring the duration he/she spends in fraternities versus sororities • The probability of a user being male, considering only session counts at fraternities and sororities • The probability of a user being male, considering only sessiondurations at fraternities and sororities

  12. Location Based Classification (LBC) • Users visiting Fraternity and/or Sorority in decreasing order of their Male probability (U1 feb2006)  • 1119 Users • 425 Males • 362 Females • P C M > 0.80and PDM > 0.80are males • PCM < 0.20and P DM < 0.20are females

  13. Group Behavior Based Filtering (GBF) • filter a user based on where his usage pattern lies with respect to all the users at a particular location • Find a Threshold • All users satisfy threshold are male or female due to the AP location • All other users are visitors

  14. Group Behavior Based Filtering (GBF) • Clustering: is dividing a set of users into several subsets such that users in each subset are most similar based on WLAN usage metrics (duration, session count, distinct login days)  • Metrics for user evaluation • Number of distinct days of login • Session count • Sum of session durations • By applying clustering technique to Sororities and Fraternity user trace from both Universities U1 and U2 • Best Cluster Size is 2 (Regular/Visitor) • Maximum width is 0.84 • Minimum width is 0.65

  15. Group Behavior Based Filtering (GBF) • Average Width for Sorority and Fraternities from University U1 and U2  Clustering results for University U1 Sororities (feb2006)

  16. Hybrid filtering (HF) • classification validation • compare the results from IBF and GBF • methods mainly select same set of users, which should be the case as both methods attempt to identify regular users • for high confidence, choose the users selected by both filtering methods • more than 90% of the users selected by GBF are common to users selected by IBF 

  17. Name Based Classification (NBC) • Usernames obtained on campuses that require authorization mechanism to access WLAN • Traces coming from university U2 provide us with usernames University U2 also host a directory that can be searched using these usernames • By Searching the directory first names corresponding to these usernames obtained • from the US Social Security administration, a list of top 1000 males and females first names is used and the names present in both lists (neutral names) are removed • this list is compared to the list obtained from university U2 directory

  18. Name Based Classification (NBC) • 11,000 out of 27,000 users classified as males or females in the trace period of Nov 2007 • 12,500 out of 30,000 users classified as males or females in the trace period of Apr 2008 • foreign national students • non-popular names 

  19. Validation of (LBC) • Validation of LBC is needed to raise confidence in the results • Three statistical methods to validate filtering mechanisms • temporal consistency: this method finds out regular users in the trace set belonging to adjacent months and compares this list to see how many are common • IBF vs GBF: this method compares results from IBF and GBF to check the similarities in the results • Cross Validation: this method takes the classification achieved using NBC method and compares it with the results of LBC

  20. Temporal Consistency Validation • Multiple one-month traces from one semester • Apply IBF, GBF and HF to find out the common users in all adjacent months before and after filtering • Becauseusers living in fraternities and sororities do not change from one month to another in the same semester,  after filtering, the percentage of common users should increase

  21. Temporal Consistency Validation • Similarity in the user population selected after filtering fraternity users for U1 

  22. IBF vs. GBF • validation mechanism that compares the results of IBF and GBF methods Comparing users selected by IBF and GBF for U1

  23. Cross Validation  • NBC  has a low error rate because of using statistics from real data coming from the US Social Security Office • Using this property of NBC, we can find out the error bound for the LBC • To calculate the error bounds, the users classified by LBC as females and males are put in sets FL and ML • Using NBC, we classify all users from Fraternities and Sororities and put them in sets FN and MN and remove unclassified users • The error in female classification by LBC • Ef = (FL∩MN)/FL • The error in male classification by LBC • Em =(ML∩FN)/ML Cross validation of LBC by NBC for U2

  24. User Behavior Analysis • Group classification to understand usage differences between groups • Gender based grouping • Male • Female • Unclassified • Groups evaluated on multiple metrics depending on the application • This paper examines the existence of differences between genders, they used the metrics • spatio-temporal distribution for wireless usage  • vendor preference

  25. User Spatial Distribution • This metrics can identify where users spend most of their time • Difference in the number of users among the genders can tell us about the building preferences of the genders • Existence of locations, which are consistently preferred by one of the two genders, highlights the existence of difference in WLAN usage by two genders

  26. User Spatial Distribution Comparison of user distribution across the university U2 campus (in Percentage) Comparison of user distribution across the university U1 campus (in Percentage)

  27. Average Duration or Temporal Analysis • Average duration of a session for males and females gives us an understanding of the extent of WLAN usage at different areas

  28. Average Duration or Temporal Analysis Average duration of male and females in different Areas of the university U2 campus  Average duration of male and females in different Areas of university U1 campus

  29. Average Duration or Temporal Analysis • Some of these differences were found to be significant and spatio-temporally consistent even across campuses; females’ wireless activity is stronger in Social Science and Sports areas, whereas males’ activity is stronger in Engineering and Music

  30. Device Preference • MAC address is used to find preferred vendors for the groups • To test whether gender provides a bias towards specific vendors, the Chi-Square statistical significance test is used • The Chi-Square test shows with 90% confidence that there is a bias between gender and vendor/brand

  31. Device Preference Device distribution by manufacturer at university U2 Device distribution by manufacturer at university U1

  32. Applications • The results from these metrics ca be applied to an application to make it context sensitive • Mobility Models • Mobility models are important tools to understand user movements and create models on which protocols can be tested • Protocol Design • Protocol and service design in Mobile Ad-Hoc networks can take features of various groups to evaluate its performance • Privacy

  33. Conclusion • This paper proposes novel methods, which use WLAN traces to classify WLAN users in to social groups based on features such as gender and study-major among others • It presents a general framework that can be applied to traces coming from multiple sources • there is a distinct difference in WLAN usage patterns for different genders even with similar population sizes

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