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The Data-Intensive Visual Analytics (DIVA) project
 

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

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 The Data-Intensive Visual Analytics (DIVA) project Presentation Transcript

    • 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 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
    • 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).
    • 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
    • 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
    • DIVA Project - Details • • • • • Process for this project (participatory design) Schema for data and provenance (ProveML) Prototype system for HTVA Constructing narratives Demo/Video
    • Workshops
    • Requirements: Data Characteristics • • • • Semi-structured Clear language Different perspectives Synthesized or derived data
    • Requirements: Uncertainty Types • • • • Source uncertainty Collection bias Spoofing or astroturfing Automated extraction of information
    • • • • • • Process for this project Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/video
    • ProveML and Facets • ProveML: Provenance XML • Facets: document, author, place, time, and theme • Review as ‘document’ Place Author Write Theme Document Time
    • 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
    • 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
    • • • • • • Process for this project Schema for data and provenance Prototype system for HTVA Constructing narratives Demo/video
    • 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/Video
    • 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/Video
    • Social Media: VAST Challenge 2011
    • 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
    • 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
    • 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