Mining behavioral groups in large wireless LANs Export

@inproceedings{hsu:mining, abstract = {Recent years have witnessed significant growth in the adoption of portable wireless communication and computing devices (e.g., laptops, PDAs, smart phones) and large-scale deployment of wireless networks (e.g., cellular, WLANs). We envision that future usage of mobile devices and services will be highly personalized. Users will incorporate these new technologies into their daily lives, and the way they use new devices and services will reflect their personality and lifestyle. Therefore it is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks and services. In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses. We represent the data using location-preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. We discover multi-modal user behavior and hundreds of distinct groups with unique behavioral patterns in both campuses, and their sizes follow a power-law distribution. Our methods and findings might provide new directions in network management and behavior-aware network protocols and applications, to name a few.}, address = {New York, NY, USA}, author = {Hsu, Wei J. and Dutta, Debojyoti and Helmy, Ahmed}, booktitle = {MobiCom '07: Proceedings of the 13th annual ACM international conference on Mobile computing and networking}, citeulike-article-id = {5248741}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1287899}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1287853.1287899}, doi = {10.1145/1287853.1287899}, isbn = {978-1-59593-681-3}, keywords = {community-detection, crawdad, dartmouth\_campus, mobility, similarity-measure, user-classification, wireless-lan}, location = {Montr\'{e}al, Qu\'{e}bec, Canada}, pages = {338--341}, posted-at = {2009-07-24 09:17:24}, priority = {2}, publisher = {ACM}, title = {Mining behavioral groups in large wireless LANs}, url = {http://dx.doi.org/10.1145/1287853.1287899}, year = {2007} }

TY - CONF ID - hsu:mining L3 - citeulike-article-id:5248741 N2 - Recent years have witnessed significant growth in the adoption of portable wireless communication and computing devices (e.g., laptops, PDAs, smart phones) and large-scale deployment of wireless networks (e.g., cellular, WLANs). We envision that future usage of mobile devices and services will be highly personalized. Users will incorporate these new technologies into their daily lives, and the way they use new devices and services will reflect their personality and lifestyle. Therefore it is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks and services. In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses. We represent the data using location-preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. We discover multi-modal user behavior and hundreds of distinct groups with unique behavioral patterns in both campuses, and their sizes follow a power-law distribution. Our methods and findings might provide new directions in network management and behavior-aware network protocols and applications, to name a few. CY - Montréal, Québec, Canada SN - 978-1-59593-681-3 AD - New York, NY, USA EP - 341 TI - Mining behavioral groups in large wireless LANs PB - ACM T2 - MobiCom '07: Proceedings of the 13th annual ACM international conference on Mobile computing and networking SP - 338 KW - community-detection KW - crawdad KW - dartmouth_campus KW - mobility KW - similarity-measure KW - user-classification KW - wireless-lan AU - Hsu, Wei AU - Dutta, Debojyoti AU - Helmy, Ahmed PY - 2007/// UR - http://dx.doi.org/10.1145/1287853.1287899 ER -