Provenance and Uncertainty in Human
Terrain Visual Analytics
Kai Xu
Middlesex University, UK
Background: DIVA Project
• DIVA: Data Intensive Visual Analytics
• EPSRC (UK Research Council) and DSTL (Defence
Science a...
Provenance
• “The place of origin or earliest
known history of something”
(Oxford Dictionary)
• “The sources of informatio...
Different Types of Provenance
• Data provenance:
– Data source and collection
– Data changes & quality issues

• Computati...
Why Provenance?
• Provide the ‘context’ of • Data/analysis quality
– Data and analysis
– Reasoning and decision

• Reprodu...
DIVA Project - Details
•
•
•
•
•

Process for this project (participatory design)
Schema for data and provenance (ProveML)...
Workshops
Requirements: Data Characteristics
•
•
•
•

Semi-structured
Clear language
Different perspectives
Synthesized or derived d...
Requirements: Uncertainty Types
•
•
•
•

Source uncertainty
Collection bias
Spoofing or astroturfing
Automated extraction ...
•
•
•
•
•

Process for this project
Schema for data and provenance
Prototype system for HTVA
Constructing narratives
Demo/...
ProveML and Facets
• ProveML: Provenance XML
• Facets: document, author, place, time, and
theme
• Review as ‘document’
Pla...
Insight as ‘Document’
Mariachi
Tequila
Shack

Place

Author

Write

Time

Pancho
Villa's
Quesadilla

Paco's Bar
and Grill
...
Mariachi
Tequila
Shack
Pancho
Villa's
Quesadilla

Mexican

Mexican food is
becoming more
popular

Paco's Bar
and Grill

Re...
•
•
•
•
•

Process for this project
Schema for data and provenance
Prototype system for HTVA
Constructing narratives
Demo/...
i
d
w
s
w
n
d
e
a
t
w

Fig. 4: Summary graphics showing the distribution of values for each

t
l
n
f
s
•
•
•
•
•

Process for this project
Schema for data and provenance
Prototype system for HTVA
Constructing narratives
Demo/...
Visual Exploration in ProveML
Collection

State

<visual encoding>

A. N. Analyst
Link to the Rest of ProveML Graph
Bookmark

Collection

State

A comment about why
this is important

A. N. Analyst
Visual Summary of a State
A Series of States
Spatial Uncertainty
Constructing Narrative
•
•
•
•
•

Process for this project
Schema for data and provenance
Prototype system for HTVA
Constructing narratives
Demo/...
Social Media: VAST Challenge 2011
Conclusions and Future Work
• Framework for provenance and uncertainty in
Human Terrain Analysis
• Some confidence that ou...
The Team
City
University
(London)
Jason Dykes

Jo Wood

Aidan Slingsby

Derek Stephens
Loughborough
University, UK

Middle...
Visit Us @ Middlesex University
• North West London: Google Map
• Interaction Design Centre
• Lots of Visual Analytics Res...
The Data-Intensive Visual Analytics (DIVA) project
The Data-Intensive Visual Analytics (DIVA) project
The Data-Intensive Visual Analytics (DIVA) project
The Data-Intensive Visual Analytics (DIVA) project
The Data-Intensive Visual Analytics (DIVA) project
The Data-Intensive Visual Analytics (DIVA) project
The Data-Intensive Visual Analytics (DIVA) project
The Data-Intensive Visual Analytics (DIVA) project
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The Data-Intensive Visual Analytics (DIVA) project

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I gave this talk when visiting Prof. Daniel Keim at University of Konstanz in Germany in July 2013

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The Data-Intensive Visual Analytics (DIVA) project

  1. 1. Provenance and Uncertainty in Human Terrain Visual Analytics Kai Xu Middlesex University, UK
  2. 2. Background: DIVA Project • DIVA: Data Intensive Visual Analytics • EPSRC (UK Research Council) and DSTL (Defence Science and Technology Lab) • Uncertainty in Human Terrain Analysis – Help ground troops understand local social structure – Working with large and heterogeneous data sets • Approach – Visual Analytic – Provenance
  3. 3. Provenance • “The place of origin or earliest known history of something” (Oxford Dictionary) • “The sources of information, such as entities and processes, involved in producing an artefact” (W3C).
  4. 4. Different Types of Provenance • Data provenance: – Data source and collection – Data changes & quality issues • Computation provenance: – Workflow – Parameters & results • Visual exploration provenance: – User interactions – Insights • Reasoning/sensemaking provenance: – Reasoning artefact: evidence, hypothesis, etc. Transformation and Analysis Data Collection Knowledge and insights Visualisation and Interaction Conclusions / Decisions Analytic Provenance
  5. 5. Why Provenance? • Provide the ‘context’ of • Data/analysis quality – Data and analysis – Reasoning and decision • Reproducibility – Trace the source – Automatic update • Help others understand the process – Collaboration – Reporting – Missing data, errors, and uncertainty – Computational analysis artefacts – Human reasoning bias • Trust – Understanding of data, analysis, and reasoning helps build the trust
  6. 6. DIVA Project - Details • • • • • Process for this project (participatory design) Schema for data and provenance (ProveML) Prototype system for HTVA Constructing narratives Demo/Video
  7. 7. Workshops
  8. 8. Requirements: Data Characteristics • • • • Semi-structured Clear language Different perspectives Synthesized or derived data
  9. 9. Requirements: Uncertainty Types • • • • Source uncertainty Collection bias Spoofing or astroturfing Automated extraction of information
  10. 10. • • • • • Process for this project Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/video
  11. 11. ProveML and Facets • ProveML: Provenance XML • Facets: document, author, place, time, and theme • Review as ‘document’ Place Author Write Theme Document Time
  12. 12. Insight as ‘Document’ Mariachi Tequila Shack Place Author Write Time Pancho Villa's Quesadilla Paco's Bar and Grill Mexican Mexican food is becoming more popular Restaurant Theme Document A. N. Analyst Insight Reviews & insights ↔ A ProveML graph
  13. 13. Mariachi Tequila Shack Pancho Villa's Quesadilla Mexican Mexican food is becoming more popular Paco's Bar and Grill Restaurant Insight A. N. Analyst Mexican food is becoming more popular A. N. Analyst Collection: all places tagged with both Mexican and Restaurant Insight
  14. 14. • • • • • Process for this project Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/video
  15. 15. i d w s w n d e a t w Fig. 4: Summary graphics showing the distribution of values for each t l n f s
  16. 16. • • • • • Process for this project Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/Video
  17. 17. Visual Exploration in ProveML Collection State <visual encoding> A. N. Analyst
  18. 18. Link to the Rest of ProveML Graph Bookmark Collection State A comment about why this is important A. N. Analyst
  19. 19. Visual Summary of a State
  20. 20. A Series of States
  21. 21. Spatial Uncertainty
  22. 22. Constructing Narrative
  23. 23. • • • • • Process for this project Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/Video
  24. 24. Social Media: VAST Challenge 2011
  25. 25. Conclusions and Future Work • Framework for provenance and uncertainty in Human Terrain Analysis • Some confidence that our work is relevant and directly related to Dstl requirements • Try ProveML with other data sets • Semantically-rich provenance in the future: infer analyst intent from actions
  26. 26. The Team City University (London) Jason Dykes Jo Wood Aidan Slingsby Derek Stephens Loughborough University, UK Middlesex University (London) William Wong Rick Walker Phong Nguyen Yongjun Zheng
  27. 27. Visit Us @ Middlesex University • North West London: Google Map • Interaction Design Centre • Lots of Visual Analytics Research – UK Visual Analytics Consortium: Oxford, Imperial, UCL, and Bangor – Visual Analytics Summer School and MSc program – MoD, EPSRC, and EU projects • Always look for collaboration

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