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measuresandmetrics_final - T race Analysis Clustering and...

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Trace Analysis, Clustering and Network Theory NOMADS
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Usage Trace in RAW Form 2007 11 17 00:00:03 EST b430bar-win-ap1200-1 [info] 189341: Nov 17 00:00:02 EST: %DOT11-6- ASSOC : Interface Dot11Radio0, Station 001b.fcb2.4fc9 Reassociated KEY_MGMT[NONE] 2007 11 17 00:00:19 EST elm-authgw-bs2100-1 [notice] user_tracking: event=user_login_successful&loglevel=notice&obj=user&ipadd r=10.249.52.174&name= bob &msg=Login RADIUS user dung on Primary RADIUS server at [ 00:1b:fc:b2:4f:c9 ]/10.249.52.174 as role Authenticated, login time = 2007-11-17 00:00:19, sessionID = 00:0E:0C:33:08:BA:119527561961645&
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Information Extraction HOST_MAC VARCHAR2(20) START_TIME DATE END_TIME DATE AAP_NAME VARCHAR2(150) START_TIMESTAMP NUMBER(38) END_TIMESTAMP NUMBER(38) ATIMEZONE VARCHAR2(20) RECORD_TYPE NUMBER(38) ATRANSACTION_ID NUMBER(38) AAP_BLDG VARCHAR2(20) DAP_BLDG VARCHAR2(20) DAP_NAME VARCHAR2(150) DTIMEZONE VARCHAR2(20) DTRANSACTION_ID NUMBER(38) ROAM_MAC VARCHAR2(20) Udayan Mapping with UF Phone Directory Number Theory and Counting
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My Experience Logic Comes First Focus on the Idea and NOT on the Data Selection of right data Selection of right Format Data Representation should be correct Start with a small Sample (training set) and move
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Most Common Form Start Time Location Duration 8175587 172.16.8.242_11006 5284 8182291 172.16.8.242_11006 14463 8243584 172.16.8.242_11006 12369 8256573 172.16.8.245_31031 20853 8283387 172.16.8.242_11006 13545
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Clustering in Spatio-Temporal Environment
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Clustering Problem: To get List of users online together Cluster size How many times? List of all locations, time etc. Challenges Each User has ~100+ sessions There are ~12000 Users Only ~5 years to finish your PhD
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Basics Rule#1: Users connected to same Access Point if(user_x. location .equals(user_y. location )){ } Rule#2: Users have intersecting time intervals if(user_x. st <= user_y. et && user_x. et >= user_y. st ){ }
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Intersecting Intervals User A: --------- -------- -------- -------- User B: --------- --------- -------- ----
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Min Heap
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Sample Output 2 6 14114 14988 17317 24220 29687 29710 2 3 5727 16499 24623 1 3 17798 21831 26081 12 5 15659 15869 19035 24539 24729 4 3 9650 13673 15720
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Possible Extensions Maintain a HeapMap / Vector of locations Maintain a timestamp schedule they met Maintain average time they met Regularity in meeting with this group or other group FFT (Yibin, Sungwook) Periodicity of meeting etc. Markov Chain Markov Models (Jeeyoung)
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Min Heap Implementation First Sort using Location Then Sort using start time User minHeap to extract the earliest finish using “End Time” Insert into minHeap using “Start Time” Add all extracted nodes into the HeapMap as key.
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