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From your FODAVA leadership team after visiting NVAC. That visualization and data analysis are not by themselves the final result or the purpose of VA, but rather it is an integrated part of iterative analytic process
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From your FODAVA leadership team after visiting NVAC • That visualization and data analysis are not by themselves the final result or the purpose of VA, but rather it is an integrated part of iterative analytic process • The most interesting parts were the interplay between the analysts and the tool builders, which made it clear that neither the data analytics part, nor the viz part, could do it alone… • So data and visual analytics is not just a disjoint union of data analytics and visualization. Rather it involves an iterative and interaction process of computer reasoning and visualization based on human reasoning • We think the three words, “Iterative, Interactive, and Integrative are important”. • I would like to add Engaging, Enlightening, and Expressive 1
Visual analytics is not a static mapVisual analytics is not information retrievalVisual analytics in not data mining 2
Visualization and Analytics Centers A Partnership with Academia,Industry, Government Laboratories Canada Alaska RVACUniversity of Washington PacificRim RVACPenn. State Drexel University NY/NJ Port AuthorityEmergency Op Center Hawaii IDS-UACUniv. of Illinois IVAC IDS-UACUniversity of Pittsburgh Europe Australia RVACStanfordUniversity Scholars IDS-UAC, Rutgers Univ. GVACs RVACPurdue University Indiana Univ.Schoolof Medicine Consortium DHS IDS-UACUniversity ofSouthern California New Zealand NSF RVACUniv. of North Carolina Charlotte, Georgia Tech Bank of America Detecting the Expected -- Discovering the UnexpectedTM
Analytic Cycle Project Map Visual Analytics Centers and Programs March 2008 Compendium SIMULATION UW2: JITC3, AR responders FINANCE Key UW1: RimSim, Simulation UG7: Financial Analytics NV: NVAC/PNNL PS: Penn State PD: Purdue SF: Stanford UG: UNCC/GT UW: U. of Washington IL: U. of Illinous PT: U. of Pittsburg RU: Rutgers US: USC DATA BASE TEMPORAL PD13:Temporal Disease Surv. UC3: Information Store SF3: Scalable Temporal Databases SURVEILLANCE PD8: Surveillance:video SENSOR MOBILE PD9: Smart Video Surv. RU7: Nuclear Sensor Detection PS10: Geo NewsWire PD4: Mobile CCI RU6: Inspection Algorithms NV5: SRS-Mobile PD6: In-Field Mobile RU9: Entropy Bio-surveillance ANIMAL AND HUMAN HEALTH MULTIMEDIA Projects are listed once, while they often could be in multiple places Vertical order has no implications e.g. Geospatial supports National Missions HETEROGENOUS/IR PD11: Zoonotic Disease Spread NV17: Audio NV14: Synthesis UG8: ResultMaps PS8: Health GeoJunction PD12: Animal Health SF2: Heterogenous Info Spaces UG4: Multimedia Analytics IL1: Search Paradigms, IR CYBER CYBER IL4: Deep Web Analytics SF1: Scalable Transactional Analytics SF5: IRIS Scalable Network Security RU1: WEB/Virtual Communities GEOSPATIAL PD14: Network Flow Security PS12: GeoViz Toolkit GEOSPATIAL/IMAGE PS11: Improvise PS15: ConceptVista PS13: CiteSpace PS9: Visual Computation PS7: Geo-Info Retrieval PS1: Geo-Knowledge OUTREACH AND EDUCATION IL6: Image Analytics UC1: Geospatial Multiple Media NV4: Education RU11: New Jersey Outreach IMAGE/VIDEO NV1: Consortium RU12: K-12 Education GRAPH AND REASONING UG5: Image/Video Theme/Temporal Analytics PD15: Education Initiative SF4: Perceptual Efficiency RU13: Undergraduates RU10: Semantic Graphs RU5: Lab for Port Security GRAPH AND REASONING RU3: Learning Decision Making UG9: Digital Library RU14: Summer Reconnect Conf UC4: Context Based Trust UG1: Reasoning Decision Making IL8: Data Science Summer Inst. NV2: Conferences PD10: Social Networks NV9: Semantic Graphs NV16: ProSPECT IL5: Streams, link analytics REGIONAL NV6: Law Enforcement PS14: SemanticNetSA UG2: STAB: Investigative Analytics UC5: E-mail Org and People Analytics RU1: Universal Information Graphs UW3: Medical Supply Analytics PS16: NeoCities PRIVACY TEXT PD3: Disaster Response VISUAL COMMUNICATION PS2: Extraction PS3: Fact Extraction PD5: Personnel Tracking PD2: Privacy and Anonymized Data NV13: Active Products PT2: Extraction Opinion RU4: Law Enforcement, Stat. Graphics PS5: Context Discovery RU8: Privacy Preserving Models PD7: Mobile: Emergency Response IL3: Contextual Text Analytics TEXT UC2: Patterns in Text UW4: Coast Guard Command VA PS6: TexPlorer UC4: Context Based Trust UG6: JIGSAW, Investigative Analytics PT3: Information Extraction NATIONAL PT1: Opinion/Sentiment Analytics DATA INGEST PS4: FEMARepVIZ NV12: Un/Str Text Analytics NV15: First Look NV11: Assessment Wall IL7: Monitoring People/Events NV19: UPA EVALUATION PD1: Data Integration UG3: Global Terrorism DB Analytics NV8: Evaluation NV7: Threat Stream Generator NV10: Electric Power Grids MATH/SEMANTIC FOUNDATIONS NV18: IN-SPIRE NV3: NSF-FODAVA Data Ingest Preparation Visual Exploration and Analytics Dissemination and Collaboration Data Representions & Transformation Developed by Jim Thomas 5/12/08
Spring/Fall Consortium and IEEE VAST 2008 • Spring VAC Consortium: May 21-22, 2008 at APL, JHU ---- Fall Nov 12, 13 in Richland Washington • IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 • http://conferences.computer.org/vast/vast2008/ • Columbus Ohio • Oct 19-24, 2008
NSF Partnership • MOU signed between DHS and NSF July 23, 2007 • 5 year agreement to forward basic science in visual analytics • Larry Rosenblum, Leader of NSF Management Team (Sankar Basu, Ephraim Glinnert, Leland Jamison, Tie Luo, Larry Rosenblum, Maria Zemakova)
Workshop Wednesday Sept. 17, 2008 • 0800 – 0900 Breakfast • 0900 – 0945 FODAVA-Lead: Missions and Plans, Haesun Park (Georgia Tech) • 0945 – 1130 Grand Tour Visual Analytics (Thomas) with Demo IEEE VAST student competition winner and discussion topic: refining Visual Analytics Methods • 1130 – 1245 Lunch (Klaus Building 1116) • 1245 – 14:15 The Depth and Breadth of Visual Analytics (Ebert) with discussion topic: Where can we have the most impact? • 14:15 - 1545 Tools for Analytical Thinking about Complex Problems (Rbarasky):, with discussion topic Developing analytic tools and methods for real applications • 1545 – 1600 Concluding Remarks • 1600 Adjourn 7
Conclusions • Visual Analytics is an opportunity worth considering • Practice of Interdisciplinary Science is required • Broadly applies to many aspects of society • For each of you: The best is yet to come…
Top Ten Challenges within Visual Analytics • Human Information Discourse for Discovery—new interaction paradigm based around cognitive aspects of critical thinking • New visual paradigms that deal with scale, multi-type, dynamic streaming temporal data flows • Data, Information and Knowledge Representation • Collaborative Predictive/Proactive Visual Analytics • Visual Analytic Method Capture and Reuse
Top Ten Challenges within Visual Analytics • Dissemination and Communication • Visual Temporal Analytics • Validation/verification with test datasets openly available • Delivering short-term products while keeping the long view • Interoperability interfaces and standards: multiple VAC suites of tools