Creative Media Days 2012 Talk on Opportunistic Activity Modeling

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

Audience Participation

Smart !!

Plethora of User Generated Trajectories

Quantifying Yourself

Activity Aware Search Experience

Better Experience with Pervasive Spaces

Contextual Notification

Predictive Appliance Management of Homes

Collective Measure

Activity Aware Recommendation

Smart Pricing

Collective Measure
Network Resource Planning

Business Intelligence

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

Opportunistic Analytics

Methodology
Data Collection
and Dimension
Reduction
Segmentation and
Behavioral
Profiling
Trajectory
Inference

On Body + Add-on Sensors are used to
collect data and ML techniques are used
to model and predict activity.
Classical Approach

Your Noise is my Signal

Location + Time + Venue Type = Activity

Location + Time + Application Type = Activity

Opportunistic Observation
By observing an individual’s engagement with semantically rich applications annotated with
temporal and spatial information, we can infer an individuals activity.

‣ 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

Case Studies
Collecting Smart Phone Trajectories was not trivial, so instead we have
collected social network and home network activity traces for our research.

Case Studies I
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”.

Density Enhancement
Multiple Traces can be combined by co-relating their spatio-temporal properties to increase
information density.

“Foursquare traces are semantically rich
and hence Geo-Tweets can be annotated
with FourSqaure activities by co-relating
spatio-temporal properties”
Shhhhhh!!

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

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)

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

Case Studies II
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

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

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.

Thank You
Email : fahim.kawsar@alcatel-lucent.com
WWW : http://www.fahim-kawsar.net
http://www.linkedin.com/in/fahimkawsar
@raswak

Creative Media Days 2012 Talk on Opportunistic Activity Modeling

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