Exploring Emergent Consumer Experience: A Topological Data Analysis Approach

© Novak and Hoffman 2015 | http://postsocial.gwu.edu
Exploring Emergent Consumer
Experience: A Topological Data
Analysis Approach
Tom Novak
Donna Hoffman
The George Washington University
Center for the Connected Consumer
CCB 2015 Annual Complexity in Business Conference, November 12-13, 2015
1

Ready or Not, The Smart Home is Coming Soon!
http://www.jklossner.com/TechToons/
2

› Setting the Stage
› A New Framework
› Uncovering the Possibility Space with Topological Data Analysis (TDA)
› TDA Application: Smart Home Sensors and Activities
› TDA Application: IFTTT Rules
Agenda
3

Setting The Stage
4

Internet Phase 1
Internet of Information
(Web)
Internet Phase 2
Internet of People
(Social)
Internet Phase 3
Internet of Things
(Post-Social)
Research Focus online experience social media
consumer experience of the
assemblage
Catchphrase
“Nobody knows you’re a dog” “On the Internet, everybody
knows you’re a dog”
“On the Internet of Things,
nobody knows you’re a
fridge”
5
From the Internet to IoT

© Novak and Hoffman 2015 | http://postsocial.gwu.edu 6
The Evolution of Interactivity
Internet Phase 1
Internet of Information
(Web)
Internet Phase 2
Internet of People
(Social)
Internet Phase 3
Internet of Things
(Post-Social)
Nature of
Interactivity
Many to many interaction
between people and
content via Web interfaces.
Google
Many to many interaction
directly between people via
social networks.
Facebook
C2M, M2M, M2P, C2C
interactions; device
interactions autonomous;
Digital → physical. Interactions
highly heterogeneous, ongoing
and evolve over time.
??
Identity of
the Internet
Shop online, browse web
pages, search for
information are all largely
static.
Global Collective Identity
Shift to smarter apps to
enable more sophisticated
and complex interactions.
Balance of power shifts from
marketer to consumer.
Global and Personal
Collective Identity
C2C interactions recede
compared to evolving
heterogeneous interactions of
C2M, M2M, M2P in
overlapping assemblages.
New Personalized Consumer
Experiences Will Emerge in the
Context of the Unique
Identities of These New
Assemblages

Why Now? The 7 Technology Laws
Technology Law Description
#1 Moore’s Law Processing power. Transistor density on integrated circuits
doubles every 12-24 months.
#2 Kryder’s Law Storage power. The density of information on hard drives
doubles every 13 months.
#3 Gilder’s Law Communications power. Total bandwidth of communication
systems doubles every 6 months.
#4 Kurzweil’s Law Accelerating returns. The time interval between salient
technology events shorter as time passes.
#5 Weiser’s Law Instant adaptation. As technology becomes ubiquitous, people
instantly adapt to new technology and take it for granted.
#6 Meeker’s Law 10x Multiplier Effect. With each new technology cycle, the
number of devices increases tenfold.
#7 Metcalfe’s Law Network power. The value of a network is proportional to the
square of the number of users.

The Consumer Internet of Things (IoT)
The collection of everyday
objects in the physical
environment, embedded
with technology including
sensors, actuators and the
ability to communicate
wirelessly with the Internet.
These devices interact and
communicate with
themselves and each other –
and with humans.
-- (Hoffman and Novak 2015)

The Internet of Things is Going to Be Huge
250 Million
Connected Cars by
2020 (Telefonica)
$19 Trillion
opportunity by 2020
(Cisco)
“100% IoT” by 2020
(CES)
Intel’s IoT group had
19% increase + $2.1
billion revenue in
2014
6.4 billion by 2016
and 21 billion by
2020 (Gartner)

A lot of hype, but so far not a lot of adoption
Industry research suggests that consumer adoption of
smart devices, the “Internet of Things,” is inevitable
(Acquity Group 2014; Affinova 2014), with ⅔ of
consumers saying they plan to buy at least one smart
home device in the next five years.
Adoption rates are low:
16% own one device and 4% own two or more
(Gartner)
6% use smart home tech (Nielsen)
4% own one device (Acquity)
Even the most aggressive projections suggest that only
30% are expected to purchase a smart thermostat (one
of the most obvious smart home applications) 5 years
from now, with much lower rates of adoption for other
smart home devices.
But the Consumer IoT Has an Adoption Problem
10

Even Early Adopters are Having Trouble

Awareness. 87% of consumers have never
heard of the “Internet of Things” and if they
have, they don’t really know what it is.
Price, Security, Privacy and a Loss of
Control. Oh, and smart home device prices are
too high, there are serious concerns about
security and privacy, and consumers worry that
smart devices may develop scary minds of their
own.
Product Value and Performance. Industry
research shows that the main barriers are a lack
of awareness and a perception that the value
proposition is missing. Many current products
simply aren’t ready for prime time.
3 Barriers to Adoption of Smart Devices
12

For the consumer IoT to expand beyond upscale consumers and techy
DIYers willing to suffer, we need to understand the value it offers to
consumers.
The current focus is on individual products (thermostat, light bulb,
refrigerator) and specific “use cases” (turn on the lights when I get
home).
Many consumers are having a hard time seeing the value from that focus.
We believe that value is embedded in what smart devices means to
consumers and want to shift the conversation to smart device identity
and consumer experience.
For this, for this we need a new framework...
Cracking the Value Code
13

Framework
14

What kind of insights can we derive about emergent
consumer experience in the IoT from all these
heterogeneous interactions?
These interactions create a whole that is more than
the sum of the parts – a set of recurrent
“assemblages” (Hoffman and Novak 2015).
Just as the web needed new frameworks for
understanding consumer experience (Hoffman and
Novak 1996), the IoT will need new frameworks to
understand the consumer experience that emerges
from these interactions.
Interactivity is Evolving and New Consumer
Experiences are Emerging
15

The Smart Home Consumer Starts with A Device or Two

The Consumer’s Focus Shifts Once There Are 3-4 Devices

People Start to Want the Devices to Talk to Each Other

Assemblage Theory. A
comprehensive theory from the
neo-realist school of philosophy
which explains the processes by
which the identity of a whole - a
whole that is more than the
sum of the parts - emerges
from on-going interactions
among its parts .
(Deleuze and Guattari 1988;
DeLanda 2002, 2006, 2011;
Harman 2008).
Smart Devices - An Assemblage of Heterogeneous Parts

CONSUMER DEVICE
From assemblage theory, interaction
involves paired capacities (Hoffman and
Novak 2015; DeLanda 2006)
capacity to affect
(IF “trigger”)
+
capacity to be affected
(THEN “action”)
The entities in an IoT interaction are either
consumers or devices (sensors, beacons,
smart products, hubs, wearables, etc.).
Interaction is the Fundamental Unit of Analysis in the
Post-Social Internet of Things (IoT)
IF I trigger a beacon
by entering a room
THEN Philips Hue
turns on the lights
DEVICE CONSUMER
IF my leak detector is
triggered by a broken
pipe
THEN my smart
home hub sends me
a text
DEVICEDEVICE
IF a motion sensor
detects activity
THEN my security
camera starts recording
Hoffman and Novak (2015) “Emergent Experience and the Connected Consumer in the Smart Home Assemblage and the IoT”

Some Interactions Define “Use Cases”
When the garage door
opens or closes -
Then turn the kitchen lights
red.
“Safety and Security”

In a 2010 White Paper, IBM introduced four areas of smart home
capabilities demonstrating early adoption: 1) entertainment and
convenience, 2) energy management, 3) safety and security, and 4)
health and welfare
In 2015, Lowe’s markets an Iris “Safe & Secure” kit, and a “Comfort
& Control” kit.
In 2015, SmartThings markets the benefits of system as “Home
Security,” “Peace of Mind,” and “Limitless Possibilities”
These benefits derive from specific use cases.
Use Cases Dominate Current Smart Home Marketing
22

But - Uses Cases Don’t Explain What Can Emerge
Safety and Security
Awareness of Energy Usage
Control and Convenience
“My house cares
about me”

The Home and Consumer Develop Emergent
Capacities From Interactions
Consumer Smart Home
remotely control camera
stream live videoview video live stream
move camera
time 1

New Capacities Emerge From Interactions
Consumer Smart Home
feel secure
move camera
enable securitytime 2
time 1

New Capacities Emerge From Interactions
Consumer Smart Home
feel secure
move camera
enable security
grant delivery person entry open front doortime 3
time 2
time 1

A Framework for Consumer Experience in the IoT
27
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2648786

Assemblage theory assumes an underlying
topological space of possibilities.
An actual assemblage is an individual singularity
that has been realized in this possibility space.
The possibility space contains universal
singularities - topological invariants – that structure
the possibility space and influence the population of
individual singularities that will emerge (DeLanda 2006, p30).
Thus, consumer experience is not only concerned
with “what is” for a given consumer (a single
consumer-IoT assemblage), but also with the
underlying structure of “what could be” for all
consumers (the larger population of all consumer IoT
assemblages).
Uncovering the Possibility Space of Consumer Experience
from Interactions in the IoT

Uncovering the Possibility Space with TDA
29

Interactivity is Evolving and New Consumer Experiences
are Emerging
What kind of insights can we derive
about emergent consumer experience
in the IoT - the “possibility space” -
from actual interactions?
These interactions represent a lot of
digital “big data” – very high
dimensionality data consisting of often
millions of ongoing interactions among
complex, heterogeneous component
devices (and consumers!).
30

Predictive Analytics approach: Fit predictive models to the data. But the complexity of the data means
hypothesis testing is often challenging. We need to know what questions to ask. Are we asking the right
questions? With big data, insights can be slow.
Conventional approaches for reduction and visualization: Use linear and nonlinear dimension
reduction techniques such as PCA, MCA, and MDS. But, even if they work, are sensitive to distance metrics
and do not preserve topological structures of the data.
Data-Driven Discovery Approach: Hypothesis-free approach based on computational topology to
qualitatively analyze functions on very high-dimensional data and visualize the data structure in low-
dimensional topological spaces. Topological data analysis (TDA) reveals structures in the data that have
invariant properties and can propel insight and improve hypothesis-generation and predictive modeling;
“digital serendipity” (Singh 2013).
132 million data points
Making Sense of of Digital Big Data from the IoT
31

TDA Framework for Generating Topological Networks
Data
Metric and
Lenses
Cover Image
Cluster
Inverse Image
Nodes and
Edges
Network
Visualization
Computational topology technique
on complex high-dim data using
unsupervised machine learning and
network visualization.
Result is 3-dim topology of
simplicial complexes (discrete,
combinatorial objects) in which
groups of data are represented
as nodes that contain rows that
are similar to each other in the
high-dim space and edges
connect nodes that share rows
(Carlsson 2009; Lum, et.al.
2012).

TDA draws on theory of topological spaces
and simplicial complexes (algebraic
topology); implementation invokes
computational topology (computational
geometry, computational complexity theory,
and computer science) – e.g. see Carlsson
2009; Lum, et.al. 2012; Singh, Memoli and
Carlsson 2007.
TDA applies a function (lens) to a data set
and builds a compressed summary of the
data.
A visual network of nodes (representing
data points) connected by edges is created
using four types of parameters:
 Metric (measure of similarity)
 Lenses (functions on the data)
 Bin resolution
 Bin overlap
How TDA Works
Slide image courtesy of Ayasdi, Inc. http://ayasdi.com/
Implementation: Ayasdi 3.0 Software Platform (ayasdi.com, Ayasdi, Inc., Menlo Park, CA) and Python Mapper (Singh, Memoli and Carlsson 2007; http://danifold.net/mapper/).

Basic Steps of Implementation: 1. Specify a Metric and Real-
Valued Functions to Calculate an Image of the Data
We choose a metric to
represent the distance or
similarity among rows.
We choose real-valued
functions (lenses) (i.e. the
image) to be applied to the
data.
Here, we map data points to
their y-coordinate value.
y-coordinate function

Basic Steps of Implementation: 2. Cover Image
We put the data into
overlapping bins based
on a chosen resolution
(number of groups for
each function) and gain
(degree of overlap of
each group within each
function).
The functions use the
metric to transform each
row in the dataset into a
single point that fall into
overlapping bins.
y-coordinate function

Basic Steps of Implementation: 3. Cluster Inverse Image
Cluster the data in the cover based on
the metric. Rows that fall within each bin
are independently clustered, using a
measure of similarity (the metric) on the
original data (i.e. the inverse image of
the functions).
Ayasdi uses single-linkage clustering with
a fixed heuristic for the choice of the
resolution parameter.
Each within-bin cluster becomes a node
in the network. Nodes represent sets of
data points that are similar with respect
to the metric.

Basic Steps of Implementation: 4. Network Visualization
Edges connect nodes with rows in
common.
Since the data were divided into overlapping
bins, a data point can be in multiple nodes.
The resulting nodes and edges
construct a topological network of
the simplicial complex.
A visualization is created whose
patterns can be interpreted for
meaning.

Metrics: correlation, Euclidean
distance, cosine, hamming, categorical
cosine, user-defined…
Functions: mean, variance, density,
centrality, PCA, MDS, user-defined…
Supervised Machine Learning
Models: TDA of machine learning
outputs with outcome variables can
enhance models through discovery of
systematic error and construction of
local models as opposed to a single
global model
Metrics and Functions

TDA Application: Smart Home Sensors and Activities
For a smart home to be smart, it needs to be able to understand what the people in the home are doing.

TDA Example #1: Smart Home Sensors and Activities
ID day/time activity sensor
1
2
Phone Call
Wash Hands
Cook
Eat
Clean
Phone Call
Wash Hands
Cook
Eat
Clean
24 participants were asked to perform 5 activities in a fixed
sequence, in an apartment equipped with motion and item sensors:
1. Make a phone call to retrieve a voicemail with a recipe
2. Wash their hands
3. Cook a pot of oatmeal according to the directions in the
voicemail
4. Eat the oatmeal
5. Clean the dishes
A total of 24 sensors recorded motion (11 sensors), items (10
sensors), and water and burner usage (3 sensors) as the
participants performed the 5 activities in sequence. Time was
recorded for each sensor event.
Data source: Dataset 1, ADL Activities, CASAS, Washington State University, http://casas.wsu.edu/datasets/
D. Cook and M. Schmitter-Edgecombe, Assessing the quality of activities in a smart environment.
Methods of Information in Medicine, 2009.
time
ID
DAY/
TIME ACTIVITY SENSOR

Map of Smart Home Sensor Locations
Phone book sensor
Oatmeal
Raisins
Brown
Sugar
Bowl Spoon
Medicine
container
Cabinet sensor
Pot
Burner
Hot
Water
Cold
Water
Motion Sensors Item Sensors
Gas & Water Sensors
Phone sensor
Data source: Dataset 1, ADL Activities, CASAS, Washington State University,
http://casas.wsu.edu/datasets/

Sample of Smart Home Event Stream Data
ID DATE TIME SENSOR ACTIVITY (“ground truth”)
1 2008-02-27 12:50:10.25482 AD1-B wash hands
1 2008-02-27 12:50:25.21543 AD1-B wash hands
1 2008-02-27 12:50:38.708635 M17 wash hands
1 2008-02-27 12:50:40.5204 AD1-B wash hands
1 2008-02-27 12:50:42.521531 M17 wash hands
1 2008-02-27 12:50:43.533263 M17 cook
1 2008-02-27 12:50:56.242 I07 cook
1 2008-02-27 12:51:01.601718 D01 cook
1 2008-02-27 12:51:03.797739 I02 cook
1 2008-02-27 12:51:05.950145 I03 cook
1 2008-02-27 12:51:09.800081 I01 cook
1 2008-02-27 12:51:10.4779 M17 cook
1 2008-02-27 12:51:11.473605 I04 cook
1 2008-02-27 12:51:12.497614 I05 cook
1 2008-02-27 12:51:16.729926 D01 cook
There are total of 6425 sensor events from 24 participants.
Data source: Dataset 1, ADL Activities, CASAS, Washington State University,
http://casas.wsu.edu/datasets/

ID activity sensor
Current
event
time
(seconds)
Window
start time
(seconds)
Window
duration
(seconds)
AD1_A
AD1_B
AD1_C
D01
E01
I01
I02
I03
I04
I05
I06
I07
I08
M01
M07
M08
M09
M13
M14
M15
M16
M17
M18
M23
asterisk
1 cook I07 166 133 33 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 167 135 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook M17 170 137 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook AD1-A 173 137 36 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 173 140 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook AD1-A 176 151 25 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-B 176 153 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 178 153 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook AD1-A 180 154 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-B 180 154 26 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-A 182 156 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-B 185 156 29 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-A 188 158 30 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-B 188 160 28 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 190 161 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook M17 194 161 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook I05 199 162 37 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-A 200 163 37 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 207 164 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook I05 207 166 41 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 213 167 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook AD1-A 215 170 45 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 219 173 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook M17 221 173 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
TIME SENSOR FOR CURRENT EVENT
43
Step 1: Create Binary Variables for Each of 24 Sensors
Sensor AD1-A (burner) was triggered at 215 seconds for participant ID = 1
Time
begins with
0 for each
participant.

ID activity sensor
Current
event
time
(seconds)
Window
start time
(seconds)
Window
duration
(seconds)
AD1_A
AD1_B
AD1_C
D01
E01
I01
I02
I03
I04
I05
I06
I07
I08
M01
M07
M08
M09
M13
M14
M15
M16
M17
M18
M23
asterisk
1 cook I07 166 133 33 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 167 135 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook M17 170 137 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook AD1-A 173 137 36 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 173 140 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook AD1-A 176 151 25 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-B 176 153 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 178 153 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook AD1-A 180 154 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-B 180 154 26 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-A 182 156 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-B 185 156 29 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-A 188 158 30 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-B 188 160 28 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 190 161 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook M17 194 161 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook I05 199 162 37 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook AD1-A 200 163 37 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 207 164 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook I05 207 166 41 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 213 167 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook AD1-A 215 170 45 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 cook M17 219 173 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
1 cook M17 221 173 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
TIME SENSOR FOR CURRENT EVENT
44
Step 2: Define Sliding Window of the Past 20 Events
Fixed window from time ti-19 to ti sums over the last 20 events, for 24 sensors
windowof20events

ID activity sensor
Current
event
time
(seconds)
Window
start time
(seconds)
Window
duration
(seconds)
AD1_A
AD1_B
AD1_C
D01
E01
I01
I02
I03
I04
I05
I06
I07
I08
M01
M07
M08
M09
M13
M14
M15
M16
M17
M18
M23
asterisk
AD1_As
AD1_Bs
AD1_Cs
D01s
E01s
I01s
I02s
I03s
I04s
I05s
I06s
I07s
I08s
M01s
M07s
M08s
M09s
M13s
M14s
M15s
M16s
M17s
M18s
M23s
asterisk_s
1 cook I07 166 133 33 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 1 0 0
1 cook M17 167 135 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 1 0 0
1 cook M17 170 137 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 10 0 0 0
1 cook AD1-A 173 137 36 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 10 0 0 0
1 cook M17 173 140 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 10 0 0 0
1 cook AD1-A 176 151 25 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook AD1-B 176 153 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook M17 178 153 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 1 0 0 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook AD1-A 180 154 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 0 0 0 1 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook AD1-B 180 154 26 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook AD1-A 182 156 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-B 185 156 29 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 2 1 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-A 188 158 30 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 3 0 0 0 0 0 2 0 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-B 188 160 28 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 4 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook M17 190 161 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 5 4 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook M17 194 161 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 5 4 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook I05 199 162 37 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 4 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-A 200 163 37 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 4 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 7 0 0 0
1 cook M17 207 164 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 6 4 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook I05 207 166 41 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 4 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 7 0 0 0
1 cook M17 213 167 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 6 4 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-A 215 170 45 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 4 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0
1 cook M17 219 173 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 7 4 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0
1 cook M17 221 173 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 6 4 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0
TIME SENSOR FOR CURRENT EVENT SENSOR SUMS IN SLIDING WINDOW
45
Step 3: Create Sums of Sensor Events in Sliding Window
Sum of the number of times each sensor was triggered in the sliding window
See: Cook, Krishnam and Rashidi (2013), Krishnan and Cook (2014), Cook and Schmitter-Edgecombe (2009)

ID activity sensor
Current
event
time
(seconds)
Window
start time
(seconds)
Window
duration
(seconds)
AD1_A
AD1_B
AD1_C
D01
E01
I01
I02
I03
I04
I05
I06
I07
I08
M01
M07
M08
M09
M13
M14
M15
M16
M17
M18
M23
asterisk
AD1_As
AD1_Bs
AD1_Cs
D01s
E01s
I01s
I02s
I03s
I04s
I05s
I06s
I07s
I08s
M01s
M07s
M08s
M09s
M13s
M14s
M15s
M16s
M17s
M18s
M23s
asterisk_s
1 cook I07 166 133 33 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 1 0 0
1 cook M17 167 135 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 1 0 0
1 cook M17 170 137 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 10 0 0 0
1 cook AD1-A 173 137 36 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 10 0 0 0
1 cook M17 173 140 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 10 0 0 0
1 cook AD1-A 176 151 25 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook AD1-B 176 153 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook M17 178 153 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 1 0 0 0 2 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook AD1-A 180 154 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 0 0 0 1 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook AD1-B 180 154 26 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 9 0 0 0
1 cook AD1-A 182 156 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 1 2 1 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-B 185 156 29 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 2 1 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-A 188 158 30 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 3 0 0 0 0 0 2 0 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-B 188 160 28 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 4 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook M17 190 161 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 5 4 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook M17 194 161 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 5 4 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook I05 199 162 37 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 4 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-A 200 163 37 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 4 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 7 0 0 0
1 cook M17 207 164 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 6 4 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook I05 207 166 41 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 4 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 7 0 0 0
1 cook M17 213 167 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 6 4 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0
1 cook AD1-A 215 170 45 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 4 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0
1 cook M17 219 173 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 7 4 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0
1 cook M17 221 173 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 6 4 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0
TIME SENSOR FOR CURRENT EVENT SENSOR SUMS IN SLIDING WINDOW
46
Step 4: Use the Data for Topological Data Analysis (TDA)
1) TDA uses rectangular matrix of events (rows) by 3 time and 24 sensor sums variables (columns)
2) Actual activity is “ground truth” (i.e., what we know and want the TDA to be able to detect).
3) The specific sensor triggered and ID are explanatory variables

Rectangular data matrix of processed sensor events using a simple baseline approach:
› Rows: current sensor event is the unit of analysis
› Columns: sums of 24 sensor events over a 20 event sliding window, time of current
event, time of first event in window, duration time of window
Distance among sensor events (rows) was obtained using the normalized correlation
metric. Columns were normalized to a mean of 0 and variance of 1, and Pearson
correlation of each pair of rows was obtained.
Ayasdi TDA lens was the x and y coordinates of a 2-dimenisonal embedding of a k-
nearest neighbors graph of the data.
TDA of Smart Home Sensor and Activity Data
Objective: Use TDA to produce a topological summary of the stream of sensor events.

Apartment Activities (Colored by Time of Events)
Metric: Normalized Correlation
Lens: K-nearest neighbors (resolution 30, gain 3)
*We used the Ayasdi 3.0 software platform
(ayasdi.com, Ayasdi, Inc., Menlo Park, CA) to
perform the TDA on the CASAS data
Blue early times
Green intermediate times
Red later times

Apartment Activities (Colored by # of Phone Call Events)
88% of Phone Call
events are in Group 1
12% of Phone Call
Look in phone book,
start phone call
M13 (dining) 48%
M14 (dining) 14%
M08 (dining) 6%
M07 (dining) 6%
M01 (dining) 6%
M09 (dining) 5%
Phone book 4%
Phone on 3%
Hang up phone,
approach kitchen
M13 (dining) 51%
M14 (dining) 29%
Phone off 21%
Note: individual sensors are shown that are triggered significantly more
often for this activity in a circled group (hypergeometric p-value <.05)

Apartment Activities (Colored by # of Wash Hands Events)
59% of Wash Hands
20% of Wash Hands
21% of Wash Hands
Near Sink
Hot Water 19%
M16 (kitchen) 13%
M15 (kitchen) 12%
M14 (dining) 12%
Near Sink
Hot Water 13%
M14 (dining) 13%
M15 (kitchen) 12%
M16 (kitchen) 10%
At Sink
Hot Water 20%
M17 (stove) 46%
M18 (sink) 26%

Apartment Activities (Colored by # of Cook Events)
98% of Cook

TDA Shows There are Two Parts to Cooking
Preparing Oatmeal
Burner 22%
Oatmeal 3%
Raisins 3%
Brown sugar 3%
Bowl 3%
Measuring spoon 3%
M17(stove) 45%
Cooking Oatmeal
Burner 33%
M17 (stove) 54%
Meal Preparation
Meal Cooking

Apartment Activities (Colored by # of Eat Events)
24% of Eat
28% of Eat
47% of Eat
Get Medicine and Water
Cold Water 13%
Medicine container 6%
Cabinet 9%
Brown sugar 3%
M16 (kitchen) 10%
M15 (kitchen) 8%
Walk to Table and Eat
M13 (dining) 26%
M14 (dining) 23%
M15 (kitchen) 11%
M16 (kitchen) 11% Eat
M14 (dining) 45%
M13 (dining) 40%
M15 (kitchen) 5%
M16 (kitchen) 5%

Apartment Activities (Colored by # of Clean Events)
68% of Clean
events are in Group 517% of Clean
15% of Clean
Put items away and start cleaning
Hot Water 24%
Cabinet 6%
Oatmeal 5%
Bowl 4%
Measuring spoon 3%
Raisins 3%
M18 (sink) 24%
Cleaning
Hot Water 32%
Cold Water 8%
M18 (sink) 11%
Cleaning
Hot Water 29%
Cold Water 15%
M18 (sink) 11%
M15 (kitchen) 4%

Implications of TDA Analysis of Smart Home Data
TDA recovers known activities based on a simple baseline method for
processing event streams using sliding windows of counts of sensors, plus
start, stop and duration time of the window.
TDA provides clear visual input on the performance of a given approach to
processing event stream data. For example, cooking has a clear structure
with 2 stages.
Next step is to improve activity recognition by modifying how the event
stream is processed, for example by using alterative window sizes, fixed vs.
variable windows, exponential time decay on event sums, sensor
dependency, past contextual information (e.g. Krishnan and Cook 2014).
Longer term: Use machine learning to predict the activities from the sensor
sums, and use TDA to understand the errors in prediction.

TDA Application: IFTTT Rules
What is the structure of the possibility space underlying the way people use IFTTT?

Most Popular IFTTT Trigger and Action Channels
IFTTT Trigger Channel IFTTT Action Channel
Feed (RSS feeds) 25% Twitter 15%
Instagram 9% Email 11%
Date & Time 8% SMS 8%
Weather 8% Evernote 7%
Facebook 5% Facebook 7%
Gmail 4% Dropbox 6%
YouTube 2% Facebook Pages 4%
Twitter 2% Google Drive 4%
WordPress 2% Tumblr 3%
Tumblr 2% Pocket 3%
Total of top 10 trigger channels
68%
Total of top 10 action channels
74%

IFTTT Data: 120,253 Rules and 1101 Variables
“Turn on my lights when I get close to
my home”
(Title Words)
IOS Location
(Trigger Channel)
You enter an area
(Trigger Words)
Phillips Hue
(Action Channel)
Turn on the lights
(Action Words)
IF
THEN
86 binary variables
69 binary variables 103 binary variables
280 binary variables
563 binary variables
Data Description
120,253 IFTTT rules were created
by 60,230 IFTTT users over a 3
year period from mid 2011 to mid
2014. (8404 unique rules)
68% of users created 1 rule
22% of users created 2-3 rules
8% of users created 4-9 rules
2% of users created 10+ rules
(23,496 total rules or 20% of data)
1101 binary variables for trigger
channels, action channels, trigger
words, action words & title words.
132 million data points

1. What IFTTT rules are people creating?
2. Are there interesting and important patterns that underlie
the IFTTT rules that have been created?
3. Do these patterns suggest emergent themes?
 Let’s take a look at some conventional data reduction and
visualization approaches first…
Preliminary Research Questions

Network Graph of 553 Nodes for 120,253 IFTTT Rules Using
All IFTTT Trigger Channels/Triggers and Action Channels/Actions
Network graph
cannot reveal
clear patterns
in these
complex data.
Only option
would be to
significantly
reduce the
number of
nodes shown.

PCA and K-Means Clustering on the Component Scores
Some general
features, but
complexity is not
revealed:
Component 1
identifies groups
of rules about
photos (+) versus
email and SMS (-)
Component 2
separates weather
and smart home
rules (+) and new
rules and feeds (-)

Multiple Correspondence Analysis of the Burt Matrix
Some gross
features, but no
complexity:
dimension 1
separates
weather (+) and
photos (1) while
dimension 2
contrasts reading
(+) and the
smart home
devices and
Android devices
(-).

Conventional dimension reduction and visualization approaches have
difficulty revealing clear patterns in these complex data of 1100+ vars.
Need about 400 dimensions to account for most of the variance in the
data. Took Stata 6 hours to compute the MCA solution.
General features that grossly separate rules are apparent, but it’s difficult
to see the more subtle behavioral patterns underlying rule creation, let
alone potential emergent themes.
Topological data analysis offers an approach to visualization of network
structure that is more revealing and useful.
TDA Provides a Different Approach

Ayadsi* TDA Solution of All 120,253 IFTTT Rules
Each node is a cluster of rules.
Nodes connect if they have
rules in common.
Color indicates the number of
rules in each node:
2790 nodes containing
118,226 rules
(98.3% of all rules)
1305 nodes containing 2028 rules
(1.7% of all rules)
1
rule
500+ rules
Metric: Hamming
Lenses: MDS 1 and MDS 2 (resolution 60, gain 1.6)
*We used the Ayasdi 3.0 software platform
(ayasdi.com, Ayasdi, Inc., Menlo Park, CA) to
perform the TDA on the IFTTT data

TDA Reveals the Possibility Space of IFTTT Rules
8
IF a new feed item
THEN send me an SMS or email notification
13,437 rules
Send me feed items
1
IF a new feed item
THEN tweet/post it or save it for later
22,227 rules
Share or save feed items
7
IF you like/tag content
THEN upload it to the cloud
4,235 rules
Store my likes
5
IF time/weather/SMS/email/location event
THEN control a device or log event
12,668 rules
Internet of Things/QS
4
IF time/weather/location event
14,265 rules
Send me event info
3
IF a new email or Craigslist post meets search conditions
7,128 rules
Send me content I’m looking for
2
IF new email, article, video
THEN save it for later
5,869 rules
Save content for later
8
IF news social media content
35,882 rules
Share or save social media
6

IFTTT Feed Rules:
Located in Two Distinct Flares in Full Network
IF a new feed item
13,437 rules
Send me feed items
1
IF a new feed item
22,227 rules
Share or save feed items
7
News Feeds are
used as triggers in
two very different
ways.
What happens if
we only analyze
the 29,990 IFTTT
rules that have
news feed as a
trigger?
Immediately consume news
Distribute or archive news

IFTTT Feed Rules:
Three Different Types in Subset Network
IF a new feed item matches my search
10,758 rules
Send me feed items I’m looking for
1
IF a new feed item
THEN share it on social media
9,530 rules
Share feed items with others
7b
IF a new feed item
THEN save it in Pocket, Buffer, Delicious etc.
6,997 rules
Save feed items for later
7a
Metric: Hamming
Lenses: MDS 1 and MDS 2 (resolution 30, gain 1.6)
Cluster 1 remains the
same (send me).
Cluster 7 splits into 7a
(save) and 7b (share). Immediately consume news
Distribute news
Archive news

In the full topological summary of 120,253 IFTTT rules, we can use
categorical variables as data lenses to separate the IFTTT rules into
groups with distinct structures.
› Social media channel lens - whether the IFTTT rule included a
social media channel (e.g. Facebook, Twitter, YouTube, etc.).
Does the structure of IFTTT rules vary based on whether the rule
includes a social media channel?
› Time lens - the year the IFTTT rule was created.
Does the structure of IFTTT rules change over time?
Using Data Lenses to Include Known Structure

Using a Data Lens to Separate Networks:
Social and Non-Social IFTTT Rules
54,300 Non-Social Rules
(45% of rules do not use a Social Media channel)
65,953 Social Rules
(55% of rule use a Social Media channel)
Metric: Hamming
Lens: MDS 1 & 2 (resolution 60, gain 1.9)
Data Lens: Social Media (2 groups)

Non-Social Rules Social Rules
Send an SMS or email if
a new feed item
Save new feed
item to read later
Save new
feed item to
cloud
Save new photo
or file to cloud
Add event to cloud from a
location or SMS/email trigger
Save tagged
or favorited
content to
cloud
Smart Home and Wearables
Day, Time and Weather
Create a
note from
email
Send email to
notify me when
something
happens
Send me email or SMS if
Craigslist or Reddit post
Send me
email or
SMS if
tagged in
Facebook
Schedule
tweet/update by
day and time
Save Facebook
or Instagram
content to cloud
Save Instagram pic
to cloud, or send to
FB or Flickr
Send Instagram
content to Twitter or
blog
Send content from
one social platform
to another
Tweet, blog,
post new
feed item

Non-Social Rules Social Rules
Smart Home and
Automation
Collect and
Save
Notify Me
Notify Me
Social
Automation
Collect and
Save
Broadcast
and Share

Non-Social
IFTTT
Rules
Social
IFTTT
Rules
Year 1
2011-12
Year 2
2012-13
Year 3
2013-14
Metric: Hamming
Lens: MDS 1 & 2 (resolution 50, gain 2.1)
Data Lens: Social Media (2 groups), Year (3 groups)
Three Years of IFTTT Rules

Non-Social
IFTTT
Rules
Social
IFTTT
Rules
Year 1
2011-12
Year 2
2012-13
Year 3
2013-14
Collect & Save
Notify Me
Automation The basic structure of IFTTT emerged in
year 1, but became more organized and
interconnected in years 2 and 3.Broadcast & Share

TDA provides a way to visualize what emerges from the IoT using
interaction events as the unit of analysis.
IFTTT is an assemblage that emerges from the capacities of the
components (i.e. triggers and actions) exercised in the interaction over
time between apps and devices that are connected through the individual
rules.
Based on our assemblage theory framework (Hoffman and Novak 2015),
the topology represents the possibility space (DeLanda 2006, 2011)
underlying the potential capacities of the IFTTT assemblage.
Supports productive hypothesis generation and subsequent predictive
modeling.
Implications of TDA Analysis of IFTTT Rules

Exploring Emergent Consumer Experience: A Topological Data Analysis A
Tom Novak and Donna Hoffman
postsocial.gwu.edu

Acknowledgments
The Ayasdi 3.0 software platform for
topological data analysis (ayasdi.com)
was used to construct all networks of
the IFTTT data. The authors
acknowledge the support of Devi
Ramanan, Global Head – Product
Collaborations, Ayasdi Inc., Menlo
Park, CA
IFTTT data were collected from a crawl
during May - June 2014 and are used
with permission of IFTTT.com, San
Francisco, CA.
Smart apartment data are from the
Center for Advanced Studies in
Adaptive Systems (CASAS), School of
Electrical Engineering and Computer
Science, Washington State University.
http://ailab.wsu.edu/casas/

Exploring Emergent Consumer Experience: A Topological Data Analysis Approach

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