S. Shumilov – Zürich Analytical Visualization Framework - a visual data processing and knowledge discovery system Ivan Denisovich, Serge Shumilov Department of Computer Science III, University of Bonn

GeoViz Workshop, March , Hamburg, Germany2 Motivation GLOWA Volta Project Integration of applications and services  data transformation  heterogeneous data  various data sources Effective visual data exploration  interactivity  tracing of decisions  specialized visualization methods Customized exploration interfaces  different interfaces for different user groups  easy programming and adaptation Solutions for analytical visualization GIS, statistical and spreadsheet software Programming libraries for visualization Visualization components frameworks Workflow composition systems

GeoViz Workshop, March , Hamburg, Germany3 Analytical Visualization Framework Visual programming facilities  data transformations  visual interfaces  visualization components Customized interactive data analysis tools and components Data exploration

GeoViz Workshop, March , Hamburg, Germany4 Demonstration

GeoViz Workshop, March , Hamburg, Germany5 Demonstration

GeoViz Workshop, March , Hamburg, Germany6 Application: M³ WATER model AVF is used for: Access to distributed models Data collection and aggregation Customized presentation interface geographic data historic and modeled climatic data Hydrological model (MIKE Basin) water supply Visualization and Analysis (AVF) transformation water demand Economical model (GAMS) demographic and economic indexes water use policies demographic and economic indexes water use policies

GeoViz Workshop, March , Hamburg, Germany7 Architecture Engine  connection of modules  notification about changes  caching of intermediate results  persistency of designs  base classes + API Visual programming workspace  graph editor  interactive manipulation  monitoring and data exploration Library of modules  generic computation  data access  spatial operations JTS API GeoTools GRIA XML Visual programming workspace AVF Module Library User- defined Standard Groups AVF Engine Java Virtual Machine

GeoViz Workshop, March , Hamburg, Germany8 Key features Interactive data manipulation  changes immediately reflected Explicit transformation chains  tracing of actions  multiple paths Modular architecture  input and output connectors Computation on demand  change  notification  demand  computation Module A … … Module B … 1. Change 2. Notification 3. Request … 4. Computation Result

GeoViz Workshop, March , Hamburg, Germany9 Benefits and Drawbacks Generic reusable modules to build complex transformations Visual programming of data transformations and interfaces Full history of applied transformations. Changes in parameters of intermediate steps result in direct recomputation High code portability ensured by Java Designs can grow large and tangled Predefined programming paradigm (execution, communication, interfaces) Inappropriate if several parameters should be changed for a new result Third-party non-Java visual libraries are hard to integrate into the framework

GeoViz Workshop, March , Hamburg, Germany10 Thank you for your attention! Questions?