A Declarative Definition Language for the Representation of Three-Dimensional Information Visualization

Transcription

1 A Declarative Definition Language for the Representation of Three-Dimensional Information Visualization Gerald Jäschke Fachgebiet Informationssysteme, Universität Duisburg-Essen, Campus Duisburg

2 Outline Introduction to Information Visualization Problem Scenario and Motivation Problem Statement and Goal State of the Art and Related Work Challenges Model and Methodology Relevance Sphere Example Seite 2

3 Information Visualization Key Approach Visualization The process of representing data as images that can aid in understanding the meaning of the data. External cognitive aids Gain insight into complex phenomena Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition. Abstract data with no natural visual representation Seite 3

4 Visualization Techniques Seite 4

5 Problem Scenario Expert Visual Data Mining Suite Information Retrieval Image Thin Client Broad Audience Information Sources Information Visualization Blueprint Thin Visualization Client Seite 5

6 Motivation The application of information visualization techniques is tightly tied to single systems Efforts in designing visualization techniques are not transferable to other systems The intention and capabilities of information visualizations are implicitly encoded knowledge inside their implementation and not accessible for further analysis Information visualization applications cannot be noted-down and communicated like documents Seite 6

7 Information Visualization Modeling Language Use Cases Use Cases Preservation Storage Archival Automatic Communication Transmission Sharing Manual Information Visualization Blueprint Use/Reuse Migration Adaptation Integration Generation Media Reasoning Validation Assessment Analysis Seite 7

8 Problem Definition, Goal, and Contributions Goal To create an extensible information visualization model and use it to represent all facets of information visualization techniques for expression, notation, preservation, and communication Information Visualization Modeling for Representation Planned Contributions Extensible information visualization model Description and ontology of information visualization Declarative formal language for representing information visualizations Reference implementation of information visualization browser Seite 8

9 Primary Challenges of Representation Information visualization modeling Adequacy and extensibility Identification of common aspects and semantics of information visualization techniques and applications Standardization of concepts Integration into one single, coherent, formal model Appropriateness to purpose Diversity Two faces to information visualization Structure modeling Process modeling Seite 9

10 Representation Approach Outline State-of-the-Art Conceptual Model Formalization Evaluation Extant Models Data Features Tasks Data Types Consolidated Information Visualization Model Information Visualization Modeling Language Communication Preservation Reasoning Interaction Visual Features Cognition <view> <technique type= clust <interaction name= <sensor ref= wi Representation for Use/Reuse Seite 10

11 Structured Overview over State-of-the-Art Seite 11

12 Conclusions from the State-of-the-Art Existing classifications focus on single facets of information visualization Existing classifications combined cover nearly the whole area of information visualization Rival models overlap Classifications vary in level of detail No single, coherent information visualization model exists No universal exchange format for information visualizations exists Seite 33

13 Modeling Language Desiderata Computational desiderata Verifiability Unambiguous representation Canonical form Inference (and variables) Expressiveness Applicational desiderata Composability Orthogonality Extensibility Bypass Authorability Able of implementation Performant Scalable Seite 34

14 Relevance Sphere - Technique Task Identify and select relevant information objects Model Query Composed of search terms T1..Tn Result Set of m information objects IO1..IOm Relevance For each information object IOp, it s relevance R to search terms T1..Tn is managed as tupel R IOp = (R IOp, T1,.., R IOp, Tn ) Information visualization techniques Infosphere Spatial substrate and graphical properties - Positioning, points, lines, volumes, enclosure Location probes and viewpoint controls - Details-on-demand (external), view coordination (external) Brushing and zooming Seite 36

15 Relevance Sphere Initial State Seite 37

16 Relevance Sphere Relevance Relation Seite 38

17 Relevance Sphere Reference Pole Weight Seite 39

18 Relevance Sphere Reference Pole Position Seite 40

19 Relevance Sphere Perspective Change Seite 41

20 Relevance Sphere Brushing Single Object Seite 42

21 Relevance Sphere Brushing Multiple Objects Seite 43

22 Relevance Sphere Brushing Multiple Objects Seite 44