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Creative Media Days 2012 Talk on Opportunistic Activity Modeling
 

Creative Media Days 2012 Talk on Opportunistic Activity Modeling

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    Creative Media Days 2012 Talk on Opportunistic Activity Modeling Creative Media Days 2012 Talk on Opportunistic Activity Modeling Presentation Transcript

    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Modeling Human Activity with Opportunistic Analytics Fahim Kawsar Scalable Systems, Bell Labs
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Bell Labs A Heritage of Innovation lightRadio UNIX Operating system C & C++ Programming languages The telephone Communications satellites Digital signal processing CCD (digital camera) Cellular telephony DSL PONWDM The TRANSISTOR The LASER LEADING EDGE RESEARCH 30,700 ACTIVE PATENTS TR50 MOST INNOVATIVE Companies 2012 8 COUNTRIES 2,900 PATENTS IN 2012 R&D BUDGET €2.3b 7 NOBEL PRIZES COLLABORATE WITH 250+ universities
    • Research by Development, KISS Principle, Top Down and End-to-End approach. Research Objective Research Methodology Design and Development of Large Scale Distributed Systems for Next Generation Communication and Data Driven Telecom Services. Scalable Systems Department
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Audience Participation
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Smart !!
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Plethora of User Generated Trajectories
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Quantifying Yourself
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Activity Aware Search Experience
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Better Experience with Pervasive Spaces
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Contextual Notification
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Predictive Appliance Management of Homes
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Collective Measure
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Activity Aware Recommendation
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Smart Pricing
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Collective Measure Network Resource Planning
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Business Intelligence
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. You Own Your Data You Sell Your Data Gerd Kortuem and Fahim Kawsar "Market-based User Innovation for the Internet of Things"; Internet of Things 2010 Conference
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Opportunistic Analytics
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Opportunistic Analytics Methodology Data Collection and Dimension Reduction Segmentation and Behavioral Profiling Trajectory Inference
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. On Body + Add-on Sensors are used to collect data and ML techniques are used to model and predict activity. Classical Approach
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Your Noise is my Signal
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Location + Time + Venue Type = Activity
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Location + Time + Application Type = Activity
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Location + Time + Application Type = Activity
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Opportunistic Observation By observing an individual’s engagement with semantically rich applications annotated with temporal and spatial information, we can infer an individuals activity. COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Opportunistic Analytics ‣ Evaluate Available Data Sources ‣ Identify Unique Characteristics ‣ Extract Relevant Data Filed ‣ Combine Co-Related Data Sources to increase information Density ‣ Perform Segmentation based on Spatial/Temporal/Activity Regularity ‣ Determine Behavioral Attributes of Different Segments Methodology ‣ Leverage Behavioral Regularity to Identify and Infer Activity Trajectory Data Collection and Dimension Reduction Segmentation and Behavioral Profiling Activity Trajectory Inference
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Case Studies COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Collecting Smart Phone Trajectories was not trivial, so instead we have collected social network and home network activity traces for our research.
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Case Studies I COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Location-Aware Social Activity
    • Dataset LBSN - Foursquare and Twitter - Traces Custom collected Geo-Fenced Tweets, and Foursquare Tweets are analyzed to construct activity trajectory. 825 Users 79431 Check-ins 1 Year 157806 Geo-Tweets 30 KM Geo tagged Tweets with embedded Foursquare check-in URL Longitude, Latitude, Timestamp, Location Name, and Category Dataset
    • Location + Time + Venue Type = Activity “Primary FourSqaure venue categories are analyzed with 325 locations to extract 10 distinct activity types”.
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Density Enhancement Multiple Traces can be combined by co-relating their spatio-temporal properties to increase information density.
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. “Foursquare traces are semantically rich and hence Geo-Tweets can be annotated with FourSqaure activities by co-relating spatio-temporal properties” Shhhhhh!!
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Density Improvement of the FourSqaure Trace by including custom annotated Geo-Tweets that were originated from previously checked-in locations. 0 10000 20000 30000 40000 50000 60000 70000 5 10 20 50 100 200 500 1000 y = 8071.1x - 4439.4 R² = 0.9654 NumberofPointsAdded Distance in Meters Enhancement of Information Density
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. 0 75 150 225 300 6 AM 8 AM 10 AM 12 PM 2 PM 4 PM 6 PM 8 PM 10 PM 12 AM 2 AM 4AM Arts and Entertainment Universities Breakfast Lunch Fast Food Dinner Outdoor Nightlife Shopping Travelling Time of the Day NoofUsers Activity Trajectory 0 75 150 225 300 6AM 8AM 10AM 12PM 2PM 4PM 6PM 8PM 10PM 12AM 2AM 4AM NoofUsers Without Density Enhancement With Density Enhancement (20m)
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Opportunistic Analytics Data Collection and Dimension Reduction ‣ Evaluate Available Data Sources ‣ Identify Unique Characteristics ‣ Extract Relevant Data Filed ‣ Combine Co-Related Data Sources to increase information Density ‣ Perform Segmentation based on Spatial/Temporal/Activity Regularity ‣ Determine Behavioral Attributes of Different Segments Methodology ‣ Leverage Behavioral Regularity to Identify and Infer Activity Trajectory Data Collection and Dimension Reduction Segmentation and Behavioral Profiling Activity Trajectory Inference
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Case Studies II COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. In-Home Web Activity
    • Dataset : Project LeYLab In-Home Internet Activity Traces Living Lab for Fiber based Services in the City of Kortrijk, Belgium. ALU 7750 Service Router with Report and Analysis Manager (RAM) was used in the backbone. 86 Households 75 Applications 60 Days 9288000 Data Points
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Heuristic : Time + Application Type = Activity “Semantically identical applications are grouped together to reduce data dimensionality, as well as to shift analysis focus to activity. 75 Applications are mapped into 8 distinct activity types”
    • Accumulated activity footprint of a representative household, activity is spread over through out the day, with higher engagements during evenings Activity Trajectory 0 5 10 15 20 25 6 AM 8 AM 10 AM 12 PM 2 PM 4 PM 6 PM 8 PM 10 PM 12 AM 2 AM 4 AM Web Communication Soical Networking Online Gaming Home Working Online Shopping Video Watching Time of the Day NoofDays
    • We have observed an inverse relationship between application usage frequency and corresponding traffic load. Accordingly, we model activity using interaction frequency and temporal regularity. This measure identifies how a household engages with a distinct activity. Recurrence Measure
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Trajectory Prediction
    • Trajectory Prediction Algorithm The algorithm predicts activity patterns of future hour slots of current day by matching patterns of similar days in the past.
    • Prediction Performance 60% of households activities can be predicted accurately 70% of times. CumulativeDistributionFunction(CDF) 0 0.2 0.4 0.6 0.8 1.0 0 0.2 0.4 0.6 0.8 1.0 F-Measure
    • Opportunistic Observation By observing an individual’s engagement with semantically rich applications annotated with temporal and spatial information, we can infer an individuals activity. Key Points Density Enhancement Multiple Traces can be combined by co-relating their spatio-temporal properties to increase information density. Trajectory Prediction The algorithm predicts activity patterns of future hour slots of current day by matching patterns of similar days in the past.
    • COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Thank You Email : fahim.kawsar@alcatel-lucent.com WWW : http://www.fahim-kawsar.net http://www.linkedin.com/in/fahimkawsar @raswak