Post on 17-Oct-2014
description
Suppor&ng Emergence: Interac&on Design for Visual Analy&cs
Approach to ESDA William Wong
Head, Interac&on Design Center Middlesex University
London, UK 15 September 2011
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NSF Workshop on
From OpenSHAPA to Open Data Sharing Arlington, VA, 15-‐16 Sep 2011
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What we do in ESDA • Tool usage in observa&on, data analysis and interpreta&on
– The resolu,on (wing touch), tool differences and hence what can be done, in different contexts eg development, learning, teaching etc
• Sharing of collected data – Why would I want to share – If I could share, what problems and hinderances
• Very insighMul of the specific challenges and nuances of use in each domain of use
• What can we learn from a different form of “ESDA” for a future OpenSHAPA / OpenSHARE? – From security and library domains – Data sharing – ‘common source’ but used by different analysts – While analysis is crucial, sense-‐making to draw conclusions based on assembled
evidence for making decisions is paramount – Use Interac,ve Visualisa,on to couple intelligent analysis (e.g. automa,c en,ty
extrac,on, automa,c thema,c analysis) with emergence driven user interface design
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Learning from a Security and Library Perspec&ve
• Making Sense – EPSRC, 9 UK Universi,es; Imperial College PI, MU Deputy PI – Mul,-‐disciplinary approach, as the problem cannot be addressed by a single
discipline e.g. image analysis, corpus linguis,cs and automated en,ty extrac,on, soVware forensics, systems engineering, representa,on design, psychology, law
• UKVAC Phase 2 – US DHS and UK HM Government, 5 UK Universi,es, Coordinator MU – Mul,-‐disciplinary approach to Nobel Laureate and FAA Flight Data Problem
• INVISQUE – JISC Rapid Innova,on Programme, MU PI – Conceived as next genera,on alterna,ve to difficult-‐to-‐use library e-‐resources =>
tangible reasoning workspace – Taylor and Francis
• Visual Analy&cs
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What is Visual Analy&cs? • Visual analy&cs is the science of analy&cal reasoning facilitated by
interac&ve visual interfaces (Thomas and Cook, 2005). – Integra,ng tools for interac,ng with the abstract human thinking and
reasoning processes – Manipula,on helps in reasoning by enabling the user to re-‐arrange the
problem space (Maglio et al, 1999) • Data graphics or info vis are sta&c • VA combines interac5ve visualiza5ons based on analy5c tools to
enable rapid querying and interroga&on of informa&on … – Visual form includes charts, network graphs, rela,onships over ,me and/
or (geographical) space – enables explora,on through rapid and repeated querying – access to original data, – analysis of data – genera,on of hypotheses – construc,on of conclusion pathways
• … for the purpose of sense-‐making – The ability to rapidly (and visually) process and assemble evidence to
enable genera,on of explana,ons or conclusions, enabling decisions 5
Frame of Reference
The Visual Analy&cs Problem: Emergence, Search and Explana&on
Lack of the ‘big picture’
Jig-‐saw puzzle (not one, but many)
Large data sets: mul,-‐sourced, mixed-‐format, silo-‐based, sta,c/stream, out of sequence, uncertain and varying quality
Keyhole problem
Visually supported analy,c reasoning Varied media, varied analysis and presenta,on tools
BLWWong©2011
20 Representa&on and Analy&c Problems 1. The problem of seeing a large data set and reasoning space through a small keyhole. 2. The problem of handling missing data. 3. The problem of handling decep&ve / misleading data. 4. The problem of handling contradictory data. 5. The problem of aggrega&ng and reconciling mul&ple points of view or predic&ons. 6. The problem of evidence colla&on and eviden&al reasoning. 7. The problem of provenance and tracing analy&c reasoning. 8. The problem of integra&ng data space, analy&c space and hypothesis spaces. 9. The problem of handling strength of evidence (including subjec&ve and objec&ve measures of strength) +
contribu&on of different pieces of evidence to a conclusion. 10. The problem of handling uncertainty in data and / or informa&on. 11. The problem of represen&ng and handling evidence over &me and space. 12. The problem of annota&ng, remembering, re-‐visi&ng, and sehng aside. 13. The problem of developing a sense of what is in the data – exploring what is there. 14. The problem of predic&ng and represen&ng emergent behaviour. 15. The problem of Iden&fying and represen&ng trends. 16. The problem of recognising and represen&ng anomalous data. 17. The problem of finding the needle in the haystack (or knowing what is chaff – i.e. info of no or low value) 18. The problem of predic&ng the path of cascading failures or effects. 19. The problem or represen&ng the sta&c and dynamic rela&onship between the data / informa&on. 20. The problem of scalability and reusability.
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Visual Analy&cs Concept
BLWWong(c)2010 8
Visualiza,on of Output
Seman,c Extrac,on
Data integra,on & transforma,on
Sensors / Surveillance / Data collec,on “SoV” Data “Hard” Data
Filters
Palerns and commonali,es
Social networks, interac,ons, ac,vi,es
Interac,ve Dynamic querying
Many tools
Architecture: Many Tools
Indexing, Structuring and Theorizing: Visual Analy&cs and OpenSHAPA
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Indexing Structuring Theorizing
Automated en,ty extrac,on
Analy,cal tools for topical, geospa,al, temporal, network analysis
Data Sets -‐ Structured
and unstructured text
-‐ Video -‐ Speech Not just reports and video, but also social media
Schema,za,on
Search and query
Colla,on
Thema,c analysis
Explana,ons Hypothesis tes,ng Eviden,al reasoning Conclusion pathways
Provenance – data, processes, and reasoning: Traceability, how did we get here?
Wong©2004 11
Activities Cues Knowledge Difficulties
Decision Strategies
Representation Design Concepts
Narratives
Transcripts
Broad Themes Related excerpts from transcripts
Specific themes Excerpts relating to specific
concepts in a theme, e.g. types of activities, examples of cues
e.g. Goals
e.g. Assessment
e.g. Planning
e.g. Control e.g. Assessment of
Situation
e.g. Assessment of Resources
Identify, Index & Collate
Structuring & Data reduction
Interpret & Conceptualise
Emergent Themes Analysis
INVISQUE demo: Interac&on Design for Suppor&ng Emergence
• INterac&ve VIsual Search and QUery Environment – Visual forms alempt to create palerns that reinforce relaIonships (CSE)
– Interac,on designed to support emergence in themaIc analysis
• INVISQUE JISC Library Version – Suppor,ng sense-‐making – Data-‐Frame Model – Using the basic interac,ve visualiza,on techniques developed here to support sense-‐making in inves,ga,ve domains
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The Interac&ve Visualiza&on Approach
• Informa&on Design Principles – Focus+Context – Proximity-‐Compa,bility Principle – Gestalt Principles of Form Percep,on – Principle of Visual Affordances – Ecological Interface Design – Representa,on of Func,onal Rela,onships
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The Interac&ve Visualiza&on Approach
• Principles implemented in design by – Anima,on, transparency, informa,on layering, spa,al layout, palern crea,on
– Emphasizing the representa,on of rela,onships within the data
– Discovery of expected and un-‐an,cipated rela,onships – Interac,on techniques enable rapid and con,nuous itera,ve querying and searching while keeping visible the context of search
– Minimizing WWILF-‐ing, or the ‘What Was I Looking For?’ problem
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The Data-‐Frame Model Guides Interac&on Design
Klein et al, 2007
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Reasoning workspace framework: Mapping and design and of reasoning work to the “keyhole”
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Analysis Space -‐ Tools and algorithms -‐ Behaviours, rela,onships and palerns -‐ what’s going on in there?
Hypothesis Space -‐ Collate, assemble, marshal -‐ Formula,on -‐ Tes,ng and simula,on -‐ arguments, conclusions, evidence
Transla&on into Design
Conclusion Pathways
BLWWong(c)2010
Data Space -‐ what’s available? -‐ What’s changed? -‐ Awareness: what’s in there?
Depic,on of “Data terrain”
“brushing”
Depic,on of “reasoning and search process”
Conclusion: Some Ques&ons
• What can we do for a future OpenSHAPA and OpenSHARE? – indexing, structuring, bearing in mind future will have lots of “smart”
analysis technologies that can support the lower levels of analysis, par,cularly indexing
• What System Architecture? – that combines data from different sources, and allows a variety of
tools to analyse and make sense of data • Alterna&ve designs for structuring and theorizing that more
directly support sense-‐making? – Adopt / adapt an interac,ve visualisa,on interface design – Focus on emergence, search and sense-‐making
• Emergence techniques such as “Temporal narra,ves” • Mul,ple threads / parallel lines of enquiry and finding intersec,ng storylines
– Reasoning workspace for assembling our thoughts and conclusions • Future work: Collabora&ve Sense-‐making environments 17
End
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