Uncertainty Computation Visualization and Validation Suresh K Lodha

Uncertainty Computation, Visualization, and Validation Suresh K. Lodha Computer Science University of California, Santa Cruz lodha@cse. ucsc. edu (831)-459 -3773

Personnel • Suresh K. Lodha – Ph. D, CS, Rice University, 1992 – Scientific & information visualization, uncertainty quantification & visualization, multi-modal visualization, computer-aided geometric design – Uncertainty research supported by National Science Foundation & Department of Energy ASCI Program (LLNL, LANL, SNL)

Overview • • • Uncertainty representation & computation Data/information fusion Quality-of-service issues Uncertainty visualization Uncertainty validation

Uncertainty Visualization Pipeline

Sources of Uncertainty • • Sensor and human limitations Noise, clutter, jamming etc. Modeling assumptions Algorithm limitations Data compression Communication errors Visualization-induced errors

Uncertainty Representation • Uncertainty formalisms used by the fusion community – Probability – Dempster-Shafer evidence theory – Fuzzy sets and possibility theory • Uncertainty representation in visualization research – Confidence intervals – Estimation error – Uncertainty range

Uncertainty Computation (Previous Work) • Data/Information Fusion – Knowledge-based systems – Random sets (Goodman, Nguyen, Mahler) • Visualization – NIST/ NCGIA `91 (Beard et al. ) – Battle. Space `98 (Durbin et al. ) – Visualization Software `96 (Globus, Uselton) – Scientific Visualization `96 -- (Lodha, Pang, Wittenbrink)

“Any battlefield necessarily deals with uncertainty, and it is necessary to determine ways to represent and encode the confidence level that exists for each piece of battlefield data. ” – Durbin et al ’ 98 (NRL)

Designing Error Metrics • • True vs. measured/observed/anticipated Observed vs. simulated High resolution vs. low resolution Continuous vs. discrete Individual source vs. multiple sources Static vs. dynamic Time-independent vs. time-critical Error-free vs. error-prone communication

Examples of Error Metrics • Local metrics -- distance metric -- curvature metric -- sampling-number or depth metric (distribution of error) • Global metrics -- Topology metric

Uncertainty Metrics : Isosurfaces

Uncertainty Metrics : Fluid Flow Topology Original 332 cp 55% 65%

Research Issues • Representations and data structures for uncertainty measures • Design and integration of error metrics • Uncertainty-aware and uncertainty-reducing data processing (algorithms and models) • Common consistent uncertainty representation over a distributed mobile network ?

Uncertainty Visualization • How to convey uncertainty to human users? – Uncluttered display – Intuitive metaphors for mapping – Data characteristics – Multi-modality • Do NOT hide processes that produce problems for the human users? – Visualize the abstraction (e. g uncertainty pipeline, graphical models)

Uncertainty Visualization • Display devices /environment – screen space (monitor, PDA, workbench, . . ) – mobility • Modality – vision – audio – haptics

Uncertainty Visualization • Data types/ characteristics – scalar/vector/tensor – discrete/continuous – static/dynamic • Levels of fusion – data-level (raw/abstract) – image-level (physical phenomena) – feature-level (compressed view) – decision-level (super-compressed)

Uncertainty Visualization (continued) • Techniques – glyphs – deformation – transparency – texture – linking – superimposing/backgrounding – augmented reality – modality

Unc Viz: Example 3 Fluid Flow Visualization

Unc Viz: Example 5 Geometric Uncertainty

Uncertainty Visualization: Example 4

Research Issues • Uncertainty mapping and metaphors for different modalities, data types and fusion tasks • Display support for a variety of uncertainty metrics/formalisms • Interactive display for uncertainty-source -> task analysis • Integration and analysis of uncertainty for decision-making?

Uncertainty Validation • Does addition of uncertainty information help human users in making decisions? • Can humans integrate qualitative and quantitative (or heterogeneous information) when there is uncertainty? – Task definitions – Careful design of experiments – Usability studies – Statistical analysis

Uncertainty Validation • Task definitions – primary level tasks (raw estimation) – secondary level tasks (correlation or simple spatiotemporal relationships) – higher level tasks • Examples – feature existence (binary decision) – feature recognition (finite multiple choices) – target aiming (zone-centered decision within a specified space-time region)

Validation Strategies • Formative vs. summative studies • With or without uncertainty mapping • Representative sampling of tasks, data and uncertainty mappings • Constrained, interactive or free-form environment • Within-subjects/between-subjects and tabular designs

Uncertainty Validation (Previous Work) • Validation of user interfaces (CHI `90 s) • Validation of multi-modal mappings (Melara, Marks, Massaro (UCSC)) • Validation of uncertainty mappings

Uncertainty Validation: Example 1 (with M. Hansen) • Protein structural alignment (intuitive metaphors)

Uncertainty Validation: Example 1 (continued) • Protein structural alignment -- accuracy of discrimination

Uncertainty Validation: Example 2 (GIS) Rainbow Saturated

Uncertainty Validation: Tasks(averaging, comparisons)

Uncertainty Validation: (with Wittenbrink & Pang) • Vector uncertainty glyph evaluation

Research Issues • Construction of user evaluation environments • Conduct user evaluation studies for efficiency and accuracy • Data analysis and statistical testing • Feedback loop to improve performance • Integrated decision tool combining uncertainty approaches in visualization and command control?

Concluding Remarks I • Provide human users with uncertainty information – Representation and computation of uncertainty – Uncertainty-aware and uncertainty-reducing algorithms and models – Uncertainty visualization – Visualization of uncertainty pipeline or hidden processes or abstract models » (continued)

Concluding Remarks II • Effective and clutter-free visualization of uncertainty along with the data/information – Sensitive to data characteristics/ fusion level/ tasks/ display environments (intuitive and cognitively accurate metaphors) – Multi-modality – Usability studies

Collaboration • Uncertainty representation/ fusion (UCSC, Syracuse GTech, UCB, USC) • Uncertainty visualization (UCSC, Gtech UCB, USC) • Multi-modal interaction (UCSC USC, GTech) • Other MURIS/ Do. D?