[part 2]


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[part 2]

  1. 1. From your FODAVA leadership team after visiting NVAC <ul><li>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 </li></ul><ul><li>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… </li></ul><ul><li>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 </li></ul><ul><li>We think the three words, “ Iterative, Interactive, and Integrative are important ” . </li></ul><ul><li>I would like to add Engaging, Enlightening, and Expressive </li></ul>
  2. 2. Visual analytics is not a static map Visual analytics is not information retrieval Visual analytics in not data mining
  3. 3. Visualization and Analytics Centers Detecting the Expected -- Discovering the Unexpected TM RVAC University of Washington RVAC Purdue University Indiana Univ. School of Medicine RVAC Univ. of North Carolina Charlotte, Georgia Tech Bank of America RVAC Penn. State DHS GVACs Scholars Consortium A Partnership with Academia, Industry, Government Laboratories Alaska New Zealand Australia Hawaii Europe Canada Pacific Rim Drexel University NY/NJ Port Authority Emergency Op Center NSF IVAC RVAC Stanford University IDS-UAC University of Southern California IDS-UAC Univ. of Illinois IDS-UAC, Rutgers Univ. IDS-UAC University of Pittsburgh
  4. 4. VISUAL COMMUNICATION NV13: Active Products Data Ingest Preparation Data Representions & Transformation Visual Exploration and Analytics Dissemination and Collaboration NATIONAL REGIONAL OUTREACH AND EDUCATION CYBER ANIMAL AND HUMAN HEALTH NV6: Law Enforcement PS16: NeoCities PD3: Disaster Response PD5: Personnel Tracking PD7: Mobile: Emergency Response UW3: Medical Supply Analytics RU4: Law Enforcement, Stat. Graphics PD11: Zoonotic Disease Spread PD12: Animal Health NV11: Assessment Wall NV10: Electric Power Grids PS4: FEMARepVIZ PS8: Health GeoJunction PD14: Network Flow Security UG6: JIGSAW, Investigative Analytics SF5: IRIS Scalable Network Security UW4: Coast Guard Command VA UG3: Global Terrorism DB Analytics UG9: Digital Library NV1: Consortium NV2: Conferences NV4: Education PD15: Education Initiative RU11: New Jersey Outreach RU12: K-12 Education RU13: Undergraduates RU14: Summer Reconnect Conf RU5: Lab for Port Security IL4: Deep Web Analytics UC5: E-mail Org and People Analytics IL7: Monitoring People/Events IL8: Data Science Summer Inst. RU1: WEB/Virtual Communities EVALUATION NV7: Threat Stream Generator NV8: Evaluation MATH/SEMANTIC FOUNDATIONS NV3: NSF-FODAVA NV12: Un/Str Text Analytics NV18: IN-SPIRE PS6: TexPlorer TEXT PS2: Extraction PS3: Fact Extraction PS5: Context Discovery IL3: Contextual Text Analytics PT2: Extraction Opinion PT3: Information Extraction UC2: Patterns in Text TEXT NV9: Semantic Graphs NV16: ProSPECT PS14: SemanticNetSA PD10: Social Networks IL5: Streams, link analytics RU3: Learning Decision Making RU10: Semantic Graphs PT1: Opinion/Sentiment Analytics UC4: Context Based Trust GRAPH AND REASONING UG2: STAB: Investigative Analytics UG1: Reasoning Decision Making GRAPH AND REASONING RU1: Universal Information Graphs UC4: Context Based Trust IMAGE/VIDEO UG5: Image/Video Theme/Temporal Analytics PS11: Improvise PS15: ConceptVista PS13: CiteSpace PS7: Geo-Info Retrieval PS12: GeoViz Toolkit PS1: Geo-Knowledge GEOSPATIAL GEOSPATIAL/IMAGE CYBER SF1: Scalable Transactional Analytics IL6: Image Analytics UC1: Geospatial Multiple Media PS9: Visual Computation MULTIMEDIA HETEROGENOUS/IR UG4: Multimedia Analytics UG8: ResultMaps SF2: Heterogenous Info Spaces IL1: Search Paradigms, IR NV14: Synthesis NV17: Audio MOBILE PD4: Mobile CCI PD6: In-Field Mobile NV5: SRS-Mobile SENSOR RU6: Inspection Algorithms RU7: Nuclear Sensor Detection RU9: Entropy Bio-surveillance TEMPORAL PD13:Temporal Disease Surv. SF3: Scalable Temporal Databases SF4: Perceptual Efficiency SIMULATION UW1: RimSim, Simulation UW2: JITC3, AR responders DATA BASE UC3: Information Store NV15: First Look NV19: UPA PD1: Data Integration DATA INGEST PRIVACY PD2: Privacy and Anonymized Data RU8: Privacy Preserving Models Analytic Cycle Project Map Visual Analytics Centers and Programs March 2008 Compendium 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 Key Projects are listed once, while they often could be in multiple places Vertical order has no implications e.g. Geospatial supports National Missions Developed by Jim Thomas 5/12/08 SURVEILLANCE PD8: Surveillance:video PD9: Smart Video Surv. PS10: Geo NewsWire FINANCE UG7: Financial Analytics
  5. 5. Spring/Fall Consortium and IEEE VAST 2008 <ul><li>Spring VAC Consortium: May 21-22, 2008 at APL, JHU ---- Fall Nov 12, 13 in Richland Washington </li></ul><ul><li>IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 </li></ul><ul><ul><ul><li>http://conferences.computer.org/vast/vast2008/ </li></ul></ul></ul><ul><ul><ul><li>Columbus Ohio </li></ul></ul></ul><ul><ul><ul><li>Oct 19-24, 2008 </li></ul></ul></ul>
  6. 6. NSF Partnership <ul><li>MOU signed between DHS and NSF July 23, 2007 </li></ul><ul><li>5 year agreement to forward basic science in visual analytics </li></ul><ul><li>Larry Rosenblum, Leader of NSF Management Team (Sankar Basu, Ephraim Glinnert, Leland Jamison, Tie Luo, Larry Rosenblum, Maria Zemakova) </li></ul>
  7. 7. Workshop Wednesday Sept. 17, 2008 <ul><li>0800 – 0900 Breakfast </li></ul><ul><li>0900 – 0945 FODAVA-Lead: Missions and Plans, Haesun Park (Georgia Tech) </li></ul><ul><li>0945 – 1130 Grand Tour Visual Analytics (Thomas) with Demo IEEE VAST student competition winner and discussion topic: refining Visual Analytics Methods </li></ul><ul><li>1130 – 1245 Lunch (Klaus Building 1116) </li></ul><ul><li>1245 – 14:15 The Depth and Breadth of Visual Analytics (Ebert) with discussion topic: Where can we have the most impact? </li></ul><ul><li>14:15 - 1545 Tools for Analytical Thinking about Complex Problems (Rbarasky):, with discussion topic Developing analytic tools and methods for real applications </li></ul><ul><li>1545 – 1600 Concluding Remarks </li></ul><ul><li>1600 Adjourn </li></ul>
  8. 8. Conclusions <ul><li>Visual Analytics is an opportunity worth considering </li></ul><ul><li>Practice of Interdisciplinary Science is required </li></ul><ul><li>Broadly applies to many aspects of society </li></ul><ul><li>For each of you: </li></ul>The best is yet to come…
  9. 9. Top Ten Challenges within Visual Analytics <ul><li>Human Information Discourse for Discovery—new interaction paradigm based around cognitive aspects of critical thinking </li></ul><ul><li>New visual paradigms that deal with scale, multi-type, dynamic streaming temporal data flows </li></ul><ul><li>Data, Information and Knowledge Representation </li></ul><ul><li>Collaborative Predictive/Proactive Visual Analytics </li></ul><ul><li>Visual Analytic Method Capture and Reuse </li></ul>
  10. 10. Top Ten Challenges within Visual Analytics <ul><li>Dissemination and Communication </li></ul><ul><li>Visual Temporal Analytics </li></ul><ul><li>Validation/verification with test datasets openly available </li></ul><ul><li>Delivering short-term products while keeping the long view </li></ul><ul><li>Interoperability interfaces and standards: multiple VAC suites of tools </li></ul>