Visual Analytics John Stasko Information Interfaces Research Group - PowerPoint PPT Presentation

April 22, 2010 Visual Analytics John Stasko Information Interfaces Research Group School of Interactive Computing Georgia Institute of Technology NSF III Workshop

Definition Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces Available at http://nvac.pnl.gov/ in PDF form 2

Alternate Definition Visual analytics combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning and decision making on the basis of very large and complex data sets Keim et al chapter in Information Visualization: Human-Centered Issues and Perspectives , 2008 3

Human-Machine Synergy • Combine strengths of both human and electronic data processing – Gives a semi-automated analytical process – Leverage what each does best From Keim 4

Main Components Interactive visualization Analytical reasoning Computational analysis Production & presentation 5

Conference Timeline Vis (scivis) InfoVis VAST 1990 1995 2006 6

Going Beyond InfoVis • Larger data, more heterogeneous • Emphasis on sense-making and analytical reasoning • Focus on complete applications 7

Encompassing Notion • VA not really an “area” per se – More of an “umbrella” or encompassing notion – Combines multiple areas or disciplines • Ultimately about using data to improve our knowledge and help make decisions 8

VA-related Areas • Visualization – InfoVis, SciVis, GIS • Data management – Databases, information retrieval, natural language • Data Analysis – Knowledge discovery, data mining, statistics • Cognitive Science – Analytical reasoning, decision-making, perception • Human-computer interaction – User interfaces, usability 9

Jigsaw Visualization for Investigative Analysis across Document Collections • Law enforcement & intelligence community • Fraud (finance, accounting, banking) • Academic research • Consumer research “Putting the pieces together” 10

The Jigsaw Team Current: and many alumni Carsten Görg Zhicheng Liu Youn-ah Kang Jaeyeon Kihm Jaegul Choo Alex Humesky Vasili Pantazopoulos Kevin Hampton 11

Problem Addressed Help “investigators” explore, analyze and understand large document collections Spreadsheets Documents/ Blogs case reports 12

Our Focus • Entities within the documents – Person, place, organization, phone number, date, email address, etc. • Entities relate/connect to each other to make a larger “story” • Connection definition: – Two entities are connected if they appear in a document together – The more documents they appear in together, the stronger the connection 13

Jigsaw • Multiple visualizations (views) of documents, entities, & their connections • Views are highly interactive and coordinated • User actions generate events that are transmitted to and (possibly) reflected in other views 14

Demo 15

More Pixels Help 16

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Document Import Various document formats with entity identification 18

Other Domains • Intelligence & law enforcement • Academic papers, PubMed • Topics on the web (medical condition) • Consumer reviews 19

To Learn More http://www.gvu.gatech.edu/ii/jigsaw 20

Acknowledgments • Work conducted as part of the Southeastern Regional Visualization and Analytics Center, supported by DHS and NVAC and the DHS Center of Excellence in Command, Control & Interoperability (VACCINE Center) • Supported by NSF IIS-0414667, CCF-0808863 (FODAVA lead), NSF IIS-0915788 • Special thanks to Remco Chang (UNCC) for the NSF data 21

End • Thanks for your attention! • Questions? 22