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Cody Dunne,  Pengyi  Zhang, Chen Huang, Cody Dunne,  Pengyi  Zhang, Chen Huang,

Cody Dunne, Pengyi Zhang, Chen Huang, - PowerPoint Presentation

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Cody Dunne, Pengyi Zhang, Chen Huang, - PPT Presentation

Jia Sun Ben Shneiderman Ping Wang amp Yan Qu cdunne bencsumdedu pengyi chhuang jsun pwang yanqu umdedu http stickischoolumdedu 28 th Annual HumanComputer Interaction Lab Symposium ID: 782664

business amp innovation intelligence2000 amp business intelligence2000 innovation data science stick umd occurrence concepts 2009 entity technology pengyi ben

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Slide1

Cody Dunne, Pengyi Zhang, Chen Huang, Jia Sun,Ben Shneiderman, Ping Wang & Yan Qu{cdunne, ben}@cs.umd.edu{pengyi, chhuang, jsun, pwang, yanqu}@umd.eduhttp://stick.ischool.umd.edu28th Annual Human-Computer Interaction Lab SymposiumMay 25-26, 2011 College Park, MD

Analyzing Trends in Science & Technology Innovation

Slide2

Business Intelligence 2000-20092006 Peak: Concept-Entity Co-OccurrenceYearFrequencyData MiningNational Security AgencyNSAWhite HouseFBIAT&TAmerican Civil Liberties UnionElectronic Frontier FoundationDept. of Homeland SecurityCIA

Slide3

Business Intelligence 2000-20092006 Peak: Entity Co-OccurrenceYearFrequencyNSANatl. Security AgencyNSAWhite HouseAT&TNatl. Security AgencyNSAAT&TNSAFBINSAEFFNSAACLU

NSACIA

AT&T

EFF

NSA

Pentagon

Pentagon

White House

AT&T

White House

ACLU

White House

Slide4

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

Slide5

Business Intelligence2000-2009:(subset)

Slide6

Business Intelligence2000-2009:Data miningNSACIAFBIWhite HousePentagonDODDHSAT&TACLUEFFSenate Judiciary Committee

Slide7

Business Intelligence2000-2009:Tech1 GoogleYahooStanfordAppleTech2IBM, CognosMicrosoftOracleFinanceNASDAQNYSESECNCRMicroStrategy

Slide8

Business Intelligence2000-2009:Air ForceArmyNavyGSAUMD*

Slide9

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

Slide10

Business Intelligence2000-2009Co-Occurrence of concepts and entities(subset)

Slide11

The STICK ProjectNSF SciSIP ProgramScience of Science & Innovation PolicyGoal: Scientific approach to science policyThe STICK ProjectScience & Technology Innovation Concept Knowledge-baseGoal: Monitoring, Understanding, and Advancing the (R)Evolution of Science & Technology Innovations

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STICK ContributionScientific, data-driven way to track innovationsVs. current expert-based, time consuming approaches (e.g., Gartner’s Hype Cycle, tire track diagrams)Includes both concept and product formsStudy relationships betweenStudy the innovation ecosystemOrganizations & peopleBoth those producing & using innovations

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ProcessCollectingProcessingVisualizing & AnalyzingCollaboratingCleaning

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CollectingIdentify ConceptsBegin with target conceptsBusiness IntelligenceHealth ITCloud ComputingCustomer Relationship ManagementWeb 2.0Develop 20-30 sub concepts from domain experts, wikisData SourcesNews DissertationAcademicPatentBlogs

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Collecting (2)Form & Expand QueriesABS("customer relationship management" OR"customers relationship management" OR"customer relation management") OR TEXT(…) OR SUB(…) OR TI(…)Scrape Results

Source: http://

xkcd.com/208

Slide16

ProcessingAutomatic Entity RecognitionBBN IdentiFinderCrowd-Sourced VerificationExtract most frequent 25%Assign to CrowdFlowerWorkers check organization names and sample sentences

Slide17

Processing (2)Compute Co-Occurrence NetworksOverall edge weightsSlice by time to see network evolutionOutputCSVGraphML

Slide18

Visualizing & AnalyzingSpotfireImport CSV, DatabaseStandard chartsMultiple coordinated viewsHighly scalableNodeXLCSV, Spigots, GraphMLAutomate featureBatch analysis & visualizationExcel 2007/2010 template

Slide19

CollaboratingOnline Research CommunityShare data, tools, resultsData & analysis downloadsSpotfire Web PlayerCommunicationCo-creation, co-authoring

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Ongoing WorkCollecting:Additional data sources and queriesProcessing:Improving entity recognition accuracyVisualizing & Analyzing:Visualizing network evolutionCo-occurrence network sliced by timeCollaborating:Develop the STICK Community siteMotivate user participationImprove the resources availableLocal testingInvitation-only testing

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Take Away MessagesEasier scientific, data-driven innovation analysis:Automatic collection & processing of innovation dataEasy access to visual analytic tools for finding clusters, trends, outliersCommunities for sharing data, tools, & results

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Cody Dunne, Pengyi Zhang, Chen Huang, Jia Sun,Ben Shneiderman, Ping Wang & Yan Qu{cdunne, ben}@cs.umd.edu{pengyi, chhuang, jsun, pwang, yanqu}@umd.eduhttp://stick.ischool.umd.eduThanks to: National Science Foundation grant SBE-0915645

Analyzing Trends in Science & Technology Innovation