Information visualization fundaments

Transcription

1 Information visualization fundaments

2 Definition (chapter Introduction and fundaments ) Visual analytics combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning and decision making on the basis of very large and complex datasets [Keim]

3 Visual analytics process Visual data exploration Visualization User interaction Preprocessing and transformation Mapping Model building Model visualization Knowledge Dataset Data mining Models Automated data analysis Parameter refinement Feedback loop

4 Visual analytics mantra Analyze first, show the important, zoom/filter, analyze further, details on demand D. A. Keim, F. Mansmann, J. Schneidewind, and H. Ziegler. Challenges in visual data analysis. In Information Visualization (IV 2006), Invited Paper, July 5-7, London, United Kingdom. IEEE Press, 2006.

5 Multidisciplinary approach Infrastructures Human perception and cognition Data management Visualization Data mining Spatio-temporal data analysis Evaluation

6 Contents Visualization need Advantages Design issues Abstraction levels Validation

7 Definition Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively [Munzner] Need Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decisionmaking methods [Munzner]

8 Need Visualizations help people analyzing data when they don't know exactly what questions they need to ask in advance External representations implemented in computers improve human capacity and allow to surpass the limitations of our internal cognition and memory.

9 Advantages of the visual channel Human visual system is a high bandwidth channel that provides to our brains an enormous quantity of information. It can be processed in parallel at the pre-conscious level or in a conscious way focusing our attention over visible objects. The input of the audio channel is perceived as a sequential stream of sounds instead of gather them during some period of time for being processed altogether. Interaction with other senses still hampers from technological issues that prevent its use outside from the research sphere.

10 Advantages of the visual channel Visualization tools help human beings in those situations where seeing data structure is better than obtaining a brief summary. For example: The Anscombe quartet. Interactivity is a key feature for building visualization tools that manage complex dataset, either because they are large enough or because they vary in time.

11 Task abstractions In the process of designing visualization tools, user tasks are as crucial as the dataset. At the higher level of abstraction, we can distinguish four categories of user tasks: presentation, discovery, enjoyment and production of new information.

12 Design considerations Effectiveness is a corollary of defining visualization to have the goal of supporting user tasks. Instead of searching the optimal solution, a more adequate goal designing visualization tools is to satisfy the requirements.

13 Design considerations We can structure the design process answering the following questions: Why users intend to use a visualization tool? What data users see? How the visual encoding and user interactions are implemented from the point of view of design choices? The procedure of answering these questions can be iteratively applied until we obtain the final design solution.

14 Design abstraction levels Problem characterization in the application domain Data and task abstractions Interaction and visual encoding Algorithmic implementation

15 Design abstraction levels These levels are nested: the output from an upstream level above is input to the downstream level below. A block is the outcome of the design process at that level, and must be validated before being used in the next design level. User intervention is critical for validating the designs in the three higher levels of abstraction. Focus design methodology through user needs is called user-centered design.

16 Problem characterization In this abstraction level we describe specific issues of the application domain and end users involved, such as the problem to solve, user demands and datasets. Each application domain has its own argot and its own workflow. Designers obtain outcomes at this level through end users interviews, through direct observation of their work in real work conditions and researching about end users work.

17 Data and task abstractions We must make abstractions of the specific tasks and data involved in the application domain and map them to generic representations independent from the concrete application domain. For tasks, we must identify the tasks required by end users in their workflow. For example: explore, compare, resume. For data, the goal will be to determine which is the representation that best fits users needs.

18 Interaction and visual encoding At this level we must determine the specific design choice for creating and manipulating the visual representations of the abstract data types that have been selected in the upper abstraction level, guided by the abstract tasks identified at that level. For visual encoding, designers define what users see. For interaction, designers decide how are dynamically managed data representations, i. e., how users change what they are seeing.

19 Algorithmic implementation In this case, the goal is to achieve an efficient implementation of the visual encoding and the interaction techniques selected in the previous abstraction level. The selection of one or another algorithm will depend on the specific requirements of the problem and the available resources.

20 Design abstraction levels: validation Risk: wrong problem characterization Validation: observe and interview end users Risk: wrong data and task abstractions Risk: inefficient interaction and visual encoding Validation: justify the visual encoding and the interaction techniques selected Risk: slow algorithm Validation: algorithm complexity analysis System implementation Validation: measure system response time and memory used Validation: quantitative and qualitative analysis of user feedback ( user study ) Validation: laboratory studies, measure of user errors and user time operation Validation: end user tests, collect proofs about the system utility Validation: field studies, usage of the deployed system Validation: measure adoption

21 Design approaches Top-down: problem-driven. Bottom-up: technique-driven.

22 References [Munzner]: Tamara Munzner. Visualization Analysis and Design. A K Peters Visualization Series. CRC Press. Nov [Anscombe]: F.J. Anscombe. Graphs in Statistical Analysis. American Statistician 27 (1973),

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25 Design Prototype implementation Analysis Validation

26 Abstract tasks Abstract data and views Why? What? How? Methods Why? What? How? Why? What? How? Why? What? How?