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The Data-Intensive Visual Analytics (DIVA) project
1) The document discusses the DIVA project which aims to help analyze uncertain human terrain data using visual analytics and provenance. 2) It introduces different types of provenance including data, computation, visual exploration, and reasoning provenance. 3) Key aspects of the project include developing a ProveML schema for representing data and provenance, prototyping a human terrain visual analytics system, and constructing narratives from the analysis.
The Data-Intensive Visual Analytics (DIVA) project
- 1. Provenance and Uncertaintyin Human Terrain Visual Analytics Kai Xu Middlesex University, UK
- 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. Provenance • “The placeof 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. Different Types ofProvenance • 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. Why Provenance? • Providethe ‘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. DIVA Project -Details • • • • • Process for this project (participatory design) Schema for data and provenance (ProveML) Prototype system for HTVA Constructing narratives Demo/Video
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- 8. Requirements: Data Characteristics • • • • Semi-structured Clearlanguage Different perspectives Synthesized or derived data
- 9. Requirements: Uncertainty Types • • • • Sourceuncertainty Collection bias Spoofing or astroturfing Automated extraction of information
- 13. • • • • • Process for thisproject Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/video
- 14. ProveML and Facets •ProveML: Provenance XML • Facets: document, author, place, time, and theme • Review as ‘document’ Place Author Write Theme Document Time
- 15. Insight as ‘Document’ Mariachi Tequila Shack Place Author Write Time Pancho Villa's Quesadilla Paco'sBar and Grill Mexican Mexican food is becoming more popular Restaurant Theme Document A. N. Analyst Insight Reviews & insights ↔ A ProveML graph
- 16. Mariachi Tequila Shack Pancho Villa's Quesadilla Mexican Mexican food is becomingmore 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
- 17. • • • • • Process for thisproject Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/video
- 20. i d w s w n d e a t w Fig. 4: Summarygraphics showing the distribution of values for each t l n f s
- 23. • • • • • Process for thisproject Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/Video
- 24. Visual Exploration inProveML Collection State <visual encoding> A. N. Analyst
- 25. Link to theRest of ProveML Graph Bookmark Collection State A comment about why this is important A. N. Analyst
- 26. Visual Summary ofa State
- 27. A Series ofStates
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- 31. • • • • • Process for thisproject Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/Video
- 32. Social Media: VASTChallenge 2011
- 33. Conclusions and FutureWork • 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
- 34. The Team City University (London) Jason Dykes JoWood Aidan Slingsby Derek Stephens Loughborough University, UK Middlesex University (London) William Wong Rick Walker Phong Nguyen Yongjun Zheng
- 35. 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