Quantified Self Comics

Quantified Self Comics
Andreas Schreiber

> QS17 > Andreas Schreiber • Quantified Self Comics > 17.06.2017DLR.de • Chart 2
Introduction
Foto: © Ann Christine Freuwörth, Wuppertal

Understand, how QS data has been produced, processed, stored,
accessed, …
Pictures from Breakout Session on Mapping Data Access (2014 QS Europe Conference, Amsterdam)
https://forum.quantifiedself.com/t/breakout-mapping-data-access/995

Example: My Weight Tracking Workflow

Questions related to QS Data and Activities
Data
• What data about the user were created during the activity X?
• What data about the user were automatically generated?
• What data about the user were derived from manual input?
Apps and Services
• Which activities support visualization of the users data?
• In which activities can the user input data?
• What processes are communicating data?
Access and Privacy
• What parties were involved in generating data X?
• What parties got access on data X?
• Can other parties see user’s data X?

Technology: Provenance of Data
Provenance is
information about entities, activities, and people
involved in
producing a piece of data or thing,
which can be used to form
assessments about its quality, reliability or trustworthiness.
PROV W3C Working Group
https://www.w3.org/TR/prov-overview

Key Concepts of Provenance Model PROV
Entities
• Physical, digital, conceptual,
or other kinds of things
• For example, documents, web sites,
graphics, or data sets
Activities
• Activities generate new entities or
make use of existing entities
• Activities could be actions or processes
Agents
• Agents takes a role in an activity and
have the responsibility for the activity
• For example, persons, pieces of software,
or organizations
Activity
Entity
Agent
wasGeneratedBy
used
wasDerivedFrom
wasAttributedTo
wasAssociatedWith

Provenance Model for Quantified Self
Sub models (templates) for basic Activities
• Input
• Sensing
• Export
• Request
• Aggregate
• Visualize
The activities generate or change data that is associated or attributed to Agents
• Users
• Software

UserData
Input
User
wasGeneratedBy
wasAssociatedWith
wasAttributedTo
prov:startTime
prov:endTime
prov:type
prov:type
prov:label
prov:time
Software
type= prov:SoftwareAgent
prov:label
wasAssociatedWith
type=prov:Person
prov:label

UserData
Visualize
User
Graphic
used
wasGeneratedBy
wasDerivedFrom
type=prov:Person
prov:label
wasAttributedTo
prov:type
prov:label
prov:type
prov:label
prov:time
prov:time
prov:type
wasAttributedTo
Software
type= prov:SoftwareAgent
prov:label
wasAssociatedWith
prov:startTime
prov:endTime
prov:type

Standard Graph Visualizations and Textual Representations of
Provenance Data are not Easy to Understand by Non-experts

Idea: Provenance Visualization Using Comics

First Sketches

Current Style

Single Comic Strip Shows a Single Data-related Action

Communicate to People Where Data is Stored

Understand How Data is Analyzed

Distinctive Features
• Shapes
• Colors
• Icons
• Letters
• Labels

Representation of Provenance Elements
Agents
Entities
Activity-related

User Study
Test subject __________ Page 1/2
Sheet 1
Please imagine the following scenario:
You wear a fitness bracelet every day,
counting the steps you take.
On your smartphone, there is an app called „StepsCompanion“,
which automatically syncs with your bracelet.
This way you can always view on your phone
how much you have already walked during the day.
You are now viewing your steps from November 16, 2016 in the app:
The button "Origin of this data" makes you curious.
So you tap it and get to see the following:
Test subject __________ Page 2/2
Please take your time to inspect and interpret these pictures.
When you are ready, please tell the examiner what the pictures convey in your understanding.
Feel free to elaborate in great detail – mention anything that occurs to you or catches your attention.

Collecting QS Provenance
Weight Tracking App
https://play.google.com/store/apps/details?id=de.medando.weightcompanion

Collecting QS Provenance
Visualization with Python Script

Date,Time,Weight,Waist,Hip,Device,Comment
"Jun 13, 2012",14:00,83.7,,,Withings,
"Jun 13, 2012",14:08,79.7,,,Withings,
"Jun 15, 2012",21:59,82.7,,,Withings,
"Jun 15, 2012",22:04,82.7,,,Withings,
"Jun 24, 2012",18:32,86.1,,,Withings,
"Jun 26, 2012",07:42,80.8,,,Withings,
"Jun 27, 2012",07:40,81.1,,,Withings,
"Jun 29, 2012",07:34,79.4,,,Withings,
"Jun 30, 2012",22:12,81.7,,,Withings,
"Jul 1, 2012",11:21,80.6,,,Withings,
"Jul 7, 2012",17:04,80.7,,,Withings,
"Jul 10, 2012",07:46,81.8,,,Withings,
"Jul 11, 2012",07:32,78.6,,,Withings,
"Jul 12, 2012",07:26,79.4,,,Withings,

PROV Comics
Web Applications
http://provcomics.de

Current Status and Future Work
References Future Work
• Schreiber A., Seider D. (2016) Towards
Provenance Capturing of Quantified Self
Data. In: Provenance and Annotation of Data
and Processes. IPAW 2016. Lecture Notes in
Computer Science, vol 9672. Springer, Cham
• Schreiber A., Struminski R. (2017) Tracing
Personal Data Using Comics. In: Universal
Access in Human-Computer Interaction.
Methods, Techniques, and Best Practices: 11th
Internatioal Conference. HCI International,
Vancouver, Canada
• Different comic styles
• Quantitative comics
• Geographical information
• Glyph-based depiction
• … and other technical improvements

Thank You!
Andreas Schreiber
www.andreas-schreiber.net
andreas.schreiber@dlr.de
@onyame

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