John Mtingwi - 16265688

Summary
It assists people analyse objects under study or examination

Visualisation
Visualisation is major area of computer graphics application. Generated images help people understand spatial and non-spatial data
Visualisation goal is to augment human capabilities in situation not well defined for computer to handle algorithmically
Visualisation allows people to off load cognition to the perceptual system
Human visual system is a very high bandwidth channel to the brain. how is it measured? whats bandwidth speed? what is the brain memory time?
keeping track of things using text is difficult. Why is it so? Graphics make tracking easy by using node-linking graphs

History
People have a history of conveying meaning through static images - continue visual communication today. why continue using history?
Creating dynamic images is a recent development because of fast computer graphics hardware algorithms. what are the  difficulties? successes?
Static Visualisation of tiny datasets can be created by hand, but computer graphics enable interactive visualisation of large datasets. why not static visual in computer graphics?
limitations - computational capacity, human perceptual and cognitive capacity and display (time & memory are limited resources). any research to improve?
Human memory is limited

Data Types
rows - items of data, columns - dimensions or attributes - using relational database approach
dimensions: quantitative, ordered and categorical, relational data or graphs
non-spatial data can be visually encoded using spatial position, coding is chosen by designer rather than given implicitly in the semantics of the dataset - central and difficult problems of visualisation design.  what are the special skills required to reduce design problems?

The most popular toolkit for spatial data is vtk, a C/C++ codebase available at - specialized area and needs specilised skills
www.vtk.org. For abstract data, the Java-based prefuse (http://www.prefuse.
org) and Processing (processing.org) toolkits are becoming widely used. The
ManyEyes site from IBM Research (www.many-eyes.com) allows people to up-
load their own data, create interactive visualizations in a variety of formats, and
carry on conversations about visual data analysis.

 

Polaris: A System for Query, Analysis, and Visualisation of Multidimensional Relational Databases
  • Polaris designed to support interactive exploration of large multidimensional relational databases. is it capable of extracting data from datawarehouses?
  • The primary interaction technique is to drag-and-drop fields from the database schema onto shelves throughout the display. This constructs a matrix of scatterplots  and an example is: showing sales versus profit for different product types in different quarters
  • The configuration of fields on shelves is called a visual specification
  • Generating Graphics - using visual specification which consists of three components (specification of different table configuration of table, type of graphic inside each pane and details of the visual encodings)
  • Steps for generating types of graph are outlined and mostly from predefined set of charts. Polaris allows analysts to construct graphics by specifying the individual components of the graphics. Are analysts allowed to modify the polaris code? or only define columns?
  • Visual mappings - each record in a pane is mapped to a mark. The components of mapping ->The type of graphic and mark and encodes fields of the records into visual or retinal properties of the selected marks - needs more explanation. do the visual properties match exactly those of human retinal properties?
  • Polaris has the ability to transform data for interactive exploration with the analyst being able to sort and filter to uncover useful relationships and information and then form ad hoc groupings
  • Polaris has the ability to generate database queries that select subsets of the data, filter, sort and group the results into panes and group, sort and aggregate the data before passing it to the graphics encoding process.
  • Polaris is useful for performing the type of exploratory data analysis advocated by statisticians. 
  • Since the publication of the article January-March 2002, what planned activities(eg database performance, leverage the direct correspondence of graphical marks in Polaris to tuples in the relational databases in order to generate database tables) have been be undertaken by now?
  • What platform was used to develop Polaris?
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