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© Hoffman and Novak 2017 | http://postsocial.gwu.edu

Visualizing Emergent Identity of Assemblages in the Internet of Things:

A Topological Data Analysis Approach

Paperpresentedat2017INFORMSMarketingScienceConference,LosAngeles,CA,June10,2017

TomNovakandDonnaHoffman,TheGeorgeWashingtonUniversity


AgendaUse Topological Data Analysis (TDA) to operationalize DeLanda’s (2002, 2006, 2011, 2016) concept of an assemblage’s possibility space(a.k.a. the market structure of underlying consumer needs).

› The Internet of Things (IOT) and IFTTT

› Topological Data Analysis (TDA)

› Analysis of IFTTT Recipes with TDA

› Future Directions

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© Hoffman and Novak 2017 | http://postsocial.gwu.edu© Hoffman and Novak 2017 | http://postsocial.gwu.edu

The IoT and IFTTT

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IFTTT Codes IoT and Web Interactions

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InternetofThings(IoT)ThewiderangeofeverydayobjectsandproductsintherealworldthatareenhancedwithprogrammablesensorsandactuatorsthatcommunicatewithotherdevicesandconsumersthroughtheInternet.--HoffmanandNovak2016

IFTTT.com


Anatomy of an IFTTT Recipe

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If any new post on Blogger then create a link post on Facebook

TriggerEvent

TriggerChannel

ActionEvent

ActionChannel

IFTTT (If-This-Then-That) Recipe


5 Years of IFTTT

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Each day, 20 million IFTTT recipes are run by IFTTT users (Lunden 2015).

Users can choose to make their recipes public by publishing them. From 2011 to 2016, a total of 331,391 IFTTT recipes have been published.

Of these published recipes, 20,675 are unique. Variants of these unique recipes have been published anywhere from 1 to 9273 times.

The 20,675 unique published IFTTT recipes use:

• 347 different trigger channels using 1110 different trigger events

• 297 different action channels using 591 different action events

Research Question: What is the topological structure of the 20,675 published IFTTT recipes?


An Assemblage Emerges from the Interaction of its Components

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Consumer Alexa

Hue IFTTTAssemblage

InteractingComponents


The Possibility Space and Assemblages

TDA provides an empirical way of visualizing the mechanism-independent possibility space, given a population of individual assemblages.

Two requirements for emergence of an assemblage (DeLanda 2011):

› Mechanism-dependent: ongoing recurrent processes involve interaction among components of an assemblage, through their exercised paired capacities.

› Mechanism-independent: a mathematical topological structure, the possibility space, contains points of attraction that guide the recurrent processes of assemblage. This leads to the emergence of populations of assemblages that wind up in the space place, although “different trajectories may be attracted to the same final state” (DeLanda 2002, p 7).

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Topological Data Analysis (TDA)

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Topological Data Analysis (TDA)

TDA (Carlsson 2009; Lum, et.al. 2012; Singh, Memoli and Carlsson2007) uses computational topology techniques on complex high-dimensional data to produce a three-dimensional 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-dimensional topological space and the edges connect nodes that share rows.

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How TDA Creates Topological Models

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X

yBi

n 1

Bin

2

Bin

3

Bin

4

Bin 1

Bin 2

Bin 3

Bin 4

Slide images adapted courtesy of Ayasdi, Inc. http://ayasdi.com/

Step 1

Rectangular data. Many rows, 2 columns x and y. When plotted, these data define a circle.

Step 2

Map data onto a single number using a function (or lens). Here, the lens is the y coordinate.

Step 3

Sort data into overlapping bins, based upon the value of the lens. Then, look at values of the original variables in these bins.

Step 4

Cluster the original data (i.e. x and y values) within each bin. Bins 1 and 4 have one cluster. Bins 2 and 3 have two clusters. This results in 6 nodes.

Step 5

Connect nodes by an edge if they have rows in common. The shape of the topological model has meaning and represents the shape of the data.


Analysis of IFTTT Recipes with TDA

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TDA Implementation

Open source

› Python Mapper (Müllner and Babu 2013; Singh, Mémoli, and Carlsson 2007)› Kepler Mapper in Python (Triskelion 2015, proof of concept for Ayasdi flavor of TDA)› Dionysus (in C++ with Python bindings) based on Zomorodian and Carlsson (2005)

and Edelsbrunner, › Letscher and Zomorodian (2000) on computing persistent homology› Package TDA - R interface for GUDHI, Dionysus, PHAT› TDA Mapper – R package using Mapper› JavaPlex library implements persistent homology for MATLAB and java-based systems› CTL = C++ library for computational topology› And others, see GitHub

Commercial

› Ayasdi (Workbench Web platform and Python SDK)

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IFTTT Recipes – Preparation for TDA20,675 unique IFTTT published recipe text strings from 2011-2016 of the form:

IF say a specific phrase using_channel Amazon Alexa THEN boost your hot water using_channel Hive Active Heating

Created binary variables for ngrams that had a frequency > 50, ignoring standard stop words:

› 1 grams (triggers n=364 and actions n=264): “Alexa”

› 2 grams (triggers n=451 and actions n=353): “Amazon Alexa”

› 3 grams (triggers n=407 and actions n=324): “Amazon Alexa add”

PCA on 2163 ngrams (of these, 880 were non-redundant)

› 362 eigenvalues > 1

› First 100 eigenvalues explained 51.04% of variance (first 2 explained 1.91%)

› Obtained scores on first 100 components (variance = eigenvalue)

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Assumptions for TDA of Our IFTTT Data

Euclidean distance was used as the metric defining distance between the rows of the data matrix.

› Why used? Each row contains scores on the first 100 principal components of ngrams, so Euclidean distance has a natural interpretation.

Neighborhood Lenses 1 & 2 (coordinates of a k-nearest neighbors graph of the data embedded in two-dimensions)

› Why used? Magnifies differentiation among groups by locally adapting distance/closeness.

Note: other metrics and lenses were also tried, but produced less clearly interpretable topological modes.

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Topological Model of 5 years of Published IFTTT Recipes from 2011-2016

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71%ofIFTTTrecipesareinnodesofonelarge

connectedcomponent

10%ofIFTTTrecipesareinnodesthataresingletons

19%ofIFTTTrecipesareinnodesof13smallconnectedcomponents

Ayasdi 6.7WorkbenchandPythonSDKusedtogeneratetopologicalmodels

© Hoffman and Novak 2017 | http://postsocial.gwu.edu© Hoffman and Novak 2017 | http://postsocial.gwu.edu 16

367 nodes contain 14,692 IFTTT recipes (71% of all recipes).Recipes can be in more than one node.Nodes are connected by an edge if they have a recipe in common.

Topological Model of 5 years of Published IFTTT Recipes from 2011-2016


Topological Models – Varying Resolution (number of bins used by TDA to generate nodes)LowResolutionModel(resolution=25)

HighResolutionModel(resolution=60)


Topological Models – Varying Gain (degree of overlap of IFTTT recipes within nodes)LowGainModel(gain=1.4)

HighGainModel(gain=2.8)


T=DateTimeA=RescueTime,LIFX,Wemo

T=AndroidDevice,Reddit,Android

BatteryA=AndroidDevice,AndroidWear

T=FacebookPages,Facebook,Tumblr,

TwitterA=Facebook,

Dropbox,OneDrive,Box,Tumblr,

WordPress,Blogger

z

T=GmailA=IF,Hue,

Boxcar,Wemo

T=Alexa,Wemo,SmartThings,Ring,ScoutAlarm,Dlink

Motion,ArloA=SmartThings,

Harmony,Manything

T=LocationT=GoogleCalendar,

Stocks,SquareA=Skype,Pushbullet

T=Fitbit,JawboneUP,

Misfit,Withings,Nike+A=Slack,Hue,IF,Gail,SMS

T=Pocket,WordPress,Tumblr,Blogger,Ebay,Instapaper,CraigslistA=Evernote,Delicious,

Diigo,Pinboard,Tumblr,WordPress,

Blogger

A=OneNote

T=AndroidSMS

A=AndroidDevice

T=Youtube,Foursquare,Vimeo,Flickr,DailyMotion

A=Pocket,Instapaper,Bitly,Tumblr,FBPages,Blogger

T=Inoreader,Tumblr,Flickr,

TwitterA=Buffer

A=GoogleDrive

T=Twitter,Life360

A=Ecobee,Tumblr

T=Automatic,Spotify,

Withings,Dash,Toodledoo,

FitBitA=GoogleCal,GoogleGlass,JawboneUp,

SMS

A=Todooist,Beminder,Toodledo

T=EmailA=Hue,Wemo,

ManyThing

T=Weather,Space

A=Hue,Nest,LittleBits,LIFX,WeMo,IF

T=IFTTT,GitHub,Wemo,Yo,

Particle,Stripe,Smappee

A=email,Gmail,Pusbullet,IFnotifications,

SMS

T=Alexa(sayspecificphrase)

T=500pxA=Feedly

Interpretation viaIFTTT Channels

GroupsofnodesidentifiedwithnetworkclusteringusingAyasdi’s CommunityAlgorithm(Louvainmodularityoptimization).


IMAGEANDVIDEOSOCIALCONTENT

SOCIALMEDIA

HOMEIOT

INFORMATION

TASKS

WEARABLEIOT

TDA Identifies 6 Broad Groups of IFTTT Recipes


Year 1

Year 3

Year 5


487 Amazon Alexa IFTTT Recipes

”Social Alexa”Consumer talks

to Alexa

”Alexa Automation”Alexa triggers other actions

Redshadingindicatesthat>50%ofrecipesinanodeuseAmazonAlexa.

TDAidentifiestwocategoriesofAlexaIFTTTrecipesoccupyingdifferentlocationsinthetopologicalmodel.

SOCIALMEDIA

HOMEIOT


TDA of Subset of 487 Alexa IFTTT Recipes

z

z

z

SocialAlexa247Recipes

AlexaAutomation168Recipes

OtherAlexaRecipes72Recipes


Future Directions

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Removing Noise in Data Provides a Clearer Topological Model

TDAof2163IFTTTngrams(HammingMetric)

TDAof100principalcomponentsof2163IFTTTngrams

(EuclideanDistanceMetric)


Preliminary Analysis of Manually Coded IFTTT Rules

TDAof455IFTTTcodesTriggernounandverbphrasecodesActionnounandverbphrasecodes

(HammingMetric)


Analysis of Manually Coded IFTTT RulesTHING TRIGGERSTHING ACTIONS

WEB TRIGGERSWEB ACTIONS

WEB TRIGGERSTHING ACTIONS

THING TRIGGERSWEB ACTIONS


Future ResearchDirections

Methodological› Comparison of alternative approaches for

processing structured text data (IFTTT recipes): N-grams, word2vec, doc2vec, topic models, latent semantic analysis, human coding.

› Comparison of alternative visualization approaches: TDA, network analysis, PCA, MDS, hierarchical and k-means clustering

Substantive› Why are certain IFTTT recipes more

frequently published, favorited and added?

› What are the dynamics of how new IFTTT recipes emerge over time?

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Acknowledgments

The Ayasdi 6.7 software platform for topological data analysis (ayasdi.com) was used to construct all topological models of the IFTTT data. The authors acknowledge the support of Devi Ramanan, Global Head – Product Collaborations, Ayasdi Inc., Menlo Park, CA

IFTTT public recipe data from 2011-2016 were provided by, and used with permission of, IFTTT.com, San Francisco, CA.


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