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What Researchers Want

Cody DunneDept. of Computer Science and

Human-Computer Interaction Lab, University of Marylandcdunne@cs.umd.edu

STM 3rd Master ClassNovember 7-9, 2011 Adelphi, MD, USA

Links from this talk:

bit.ly/stmwant

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Researchers want to…

1. Find a specific paper2. Explore a research area3. Do retrospective analysis4. Share their results

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1. Find a specific paper

• Metadata or PDF?• From memory (search)• From reference list– DOI/URL– Search

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2. Exploring a research area

• Foundations• Emerging research topics• State of the art/open problems• Collaborations & relationships between

Communities• Field evolution• Easily understandable surveys

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User requirements• Control over the paper collection– Choose custom subset via query, then iteratively drill down,

filter, & refine• Overview either as visualization or text statistics– Orient within subset

• Easy to understand metrics for identifying interesting papers– Ranking & filtering

• Create groups & annotate with findings– Organize discovery process– Share results

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Action Science Explorer

• Bibliometric lexical link mining to create a citation network and citation context

• Network clustering and multi-document summarization to extract key points

• Potent network analysis and visualization tools

www.cs.umd.edu/hcil/ase

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Reference management & grouping

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Citation network overviewCommunities, outliers, invalid data

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Statistics & visualization

• Network statistics– Degree– Betweenness– Closeness– Pagerank

• Attributes– Year– Downloads– Citations– References

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Field evolution

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Citation context & summarization

• Citation context– Key contributions– Critical reception– Citations to subsequent/similar work

• Hyperlinked citations in text– See surrounding context of citation– View cited papers while reading

• Multi-document summarization– Citation context– Abstract– Full text

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3. Retrospective analysis

• Automatic collection & processing of bibliometric data

• Easy access to visual analytic tools for finding clusters, trends, outliers

• Communities for sharing data, tools, & results

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STICK Project

• Scientific, data-driven way to track innovations– Vs. current expert-based, time consuming

approaches (e.g., Gartner’s Hype Cycle, tire track diagrams)

• Includes both concept and product forms– Study relationships between

• Study the innovation ecosystem– Organizations & people– Both those producing & using innovations

stick.ischool.umd.edu

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Case study: tree visualization

• Problem: Traditional 2D node-link diagrams of trees become too large

• Solutions:– Treemaps: Nested Rectangles– Cone Trees: 3D Interactive Animations– Hyperbolic Trees: Focus + Context

• Measures:– Papers, articles, patents, citations,…– Press releases, blog posts, tweets,…– Users, downloads, sales,…

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Treemaps: nested rectangles

www.cs.umd.edu/hcil/treemap-history

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Smartmoney MarketMap Feb 27, 2007

smartmoney.com/marketmap

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Cone trees: 3D interactive animations

Robertson, G. G., Card, S. K., and Mackinlay, J. D., Information visualization using 3D interactive animation, Communications of the ACM, 36, 4 (1993), 51-71.

Robertson, G. G., Mackinlay, J. D., and Card, S. K., Cone trees: Animated 3D visualizations of hierarchical information, Proc. ACM SIGCHI Conference on Human Factors in Computing Systems, ACM Press, New York, (April 1991), 189-194.

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Hyperbolic trees: focus & context

Lamping, J. and Rao, R., Laying out and visualizing large trees using a hyper-bolic space, Proc. 7th Annual ACM symposium on User Interface Software and Technology, ACM Press, New York (1994), 13-14.

Lamping, J., Rao, R., and Pirolli, P., A focus+context technique based on hy-perbolic geometry for visualizing large hierarchies, Proc. SIGCHI Conference on Human Factors in Computing Systems, ACM Press, New York (1995), 401-408.

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Case study: tree visualization impact

TM=TreemapsCT=Cone TreesHT=Hyperbolic Trees

Trad

e Pr

ess

Artic

les

Acad

emic

Pa

pers

Pate

nts

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Case study: tree visualization citationsTM=TreemapsCT=Cone TreesHT=Hyperbolic Trees

Acad

emic

Pa

pers

Pate

nts

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Case study: business intelligence

Year

Proquest News 2000-2009Co-occurrence of concepts with organizations

Data Mining• National Security Agency• White House• FBI• AT&T• American Civil Liberties Union• Electronic Frontier Foundation• Dept. of Homeland Security• CIA

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Business Intelligence2000-2009Matrix showing Co-Occurrence of concepts and entities

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Business Intelligence2000-2009:(subset)

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Business Intelligence2000-2009:Data mining• NSA• CIA• FBI• White House• Pentagon• DOD• DHS• AT&T• ACLU• EFF• Senate Judiciary

Committee

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Business Intelligence2000-2009:Tech1 • Google• Yahoo• Stanford• AppleTech2• IBM, Cognos• Microsoft• OracleFinance• NASDAQ• NYSE• SEC• NCR• MicroStrategy

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Business Intelligence2000-2009:• Air Force• Army• Navy• GSA• UMD*

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STICK Process

• News • Dissertation• Academic

• Patent

• Blogs

• Identify concepts• Query data sources• Processing

• Automatic entity recognition• Crowd-sourced verification• Co-occurrence networks

• Visualizing & analyzing• Overall statistics• Co-occurrence networks• Network evolution

• Sharing results

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4. Sharing results

• Easily usable metadata (BibTeX, EndNote, etc.)• Collaborative authoring• Online communities

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Collaborative literature reviews• Organized references• Annotated PDFs

www.mendeley.com

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Shared data & analysis repositories

stick.ischool.umd.edu/community

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Researchers want to…

1. Find a specific paper2. Explore a research area3. Do retrospective analysis4. Share their results

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What Researchers Want

Cody DunneDept. of Computer Science and

Human-Computer Interaction Lab, University of Marylandcdunne@cs.umd.edu

This work has been partially supported by NSF grants IIS 0705832 (ASE) and

SBE 0915645 (STICK)

Links from this talk:

bit.ly/stmwant