{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/41615"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/41615","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Discovering user context with mobile devices : location and time","abstract":"Life for many people is based on a set of daily routines, such as home, work, and leisure. If the activities in life occur in recurring patterns, then the context in which they occur should also follow a pattern. In this thesis, we explore using cell phones for learning recurring locations using only a timestamped history of the cell tower the device is connected to. We base our approach on an existing graph-based online algorithm, but modify it to compute additional statistics for offline analysis to obtain better results. We then further refine the offline algorithm to include time-partitioned nodes to resolve some observed shortcomings. Finally, we evaluate all three algorithms on a dataset of GSM readings over a one month period, and show how our successive modifications improved the locations found.","abstract_html":"Life for many people is based on a set of daily routines, such as home, work, and leisure. If the activities in life occur in recurring patterns, then the context in which they occur should also follow a pattern. In this thesis, we explore using cell phones for learning recurring locations using only a timestamped history of the cell tower the device is connected to. We base our approach on an existing graph-based online algorithm, but modify it to compute additional statistics for offline analysis to obtain better results. We then further refine the offline algorithm to include time-partitioned nodes to resolve some observed shortcomings. Finally, we evaluate all three algorithms on a dataset of GSM readings over a one month period, and show how our successive modifications improved the locations found.","abstract_has_math":false,"creators":["Song, Ning, M. Eng. Massachusetts Institute of Technology"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Larry Rudolph."],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006","date_published":"2006","updated_at":"2026-07-22T22:22:26Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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If the activities in life occur in recurring patterns, then the context in which they occur should also follow a pattern. In this thesis, we explore using cell phones for learning recurring locations using only a timestamped history of the cell tower the device is connected to. We base our approach on an existing graph-based online algorithm, but modify it to compute additional statistics for offline analysis to obtain better results. We then further refine the offline algorithm to include time-partitioned nodes to resolve some observed shortcomings. Finally, we evaluate all three algorithms on a dataset of GSM readings over a one month period, and show how our successive modifications improved the locations found."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Discovering user context with mobile devices : location and time"]}]}],"canonical_facts":{"dc:contributor.advisor":["Larry Rudolph."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Song, Ning, M. Eng. Massachusetts Institute of Technology"],"dc:date.accessioned":["2008-05-19T16:01:06Z"],"dc:date.available":["2008-05-19T16:01:06Z"],"dc:date.issued":["2006"],"dc:description":["Thesis (M. 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Finally, we evaluate all three algorithms on a dataset of GSM readings over a one month period, and show how our successive modifications improved the locations found."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/41615"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Discovering user context with mobile devices : location and time"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:22:26Z"}