TopicPresentation_Group3 - T echniques for mining per iodic...

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Techniques for mining periodic user behavior in WLAN traces Group 3 Kes Peart mukundh mohan upanita goswami Srikanth Subramanian Natarajan Chockalingam
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Analysis of WLAN Traces Popularity of APs in MANETS Paper: Periodic properties of user mobility and access-point popularity By: Minkyoung Kim and David Kotz.
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What will be presented Trace Acquisition The DFT APs Periodicity Using one month traces Using one year traces Access point clustering Significance of findings Summary
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Trace Aquisition Traces are from Dartmouth Campus Collected over spring 2003 and winter 2004 APs having Less that 50 user/hr were removed. Such APs are considered inactive hence irrelevant to the study. Only Unique users at each AP was considered.
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The DFT (Discrete Fourier Transform) Why the DFT was used: Transform traces to frequency domain hence exposing periodic behavior Easy to get back to time domain by taking the inverse DFT
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AP Popularity Distribution Of user One Access Point Vertical lines represent number of
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AP Periodicity (base on four week trace) 85% of APs had their primary period at one day 25% of APs had their primary period at one week
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AP Periodicity (base on one year trace) 25% of APs had their period at one day 38% had their period ad one week
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Clustering of APs Why cluster APS? Clustering aims at discovering”natural” classes in data. Discovery of structure in data can lead to a new understanding of data.
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Clustering (contd) Clustering was done using the autoclass Autoclass takes three parameters APs primary period APs secondary period Maximum amount of users AP serviced per hour
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Significance of Findings Create mathematical equation base on periodic behavior of access points
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Summary The following was presented: Information on traces The DFT Periodicity of APs AP classification Interpretation on findings
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Thank You
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Guanling Chen, Heng Huang, and Minkyong Kim
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Introduction a Offline datamining techniques are used to study periodic patterns in a campus wide environment. a Profiling client’s movements lead to Location prediction Anomaly detection Mobility Modeling a Challenges addressed in this paper : Removing noise from data Interpretation of discovered patterns
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Data preparation and the ping pong effect a Only clients who were active for more than 30 days were considered. a Diameter as a client’s mobility measure. a The ping pong effect causes the changing of a client’s association from one AP to another AP when there is no physical movement. a Heuristic approach used to prevent the ping pong effect Grouping of Aps that the client associates often [Switching back and forth] Sorting out the most prominent AP from this group for the particular user a Thus finding the appropriate clients who have meaningful movement patterns and reducing noise
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Q Each group has a “significant
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