On October 1st 2009 I will give an invited talk at the Trinity Long Room Hub entitled 'Using Information Visualisation as an Analytical Tool' at 1.00 p.m. - 2.30 p.m. IIIS Seminar Room, C.6002, 6th Floor, Arts Building, TCD.
The following is the working abstract for the talk.
A byproduct of the explosive growth in the use of computing technology is that organizations are generating, gathering, using and storing data at an increasing rate. Consider the amount of data a Government census collect, the amount of data Google gathers and uses or details of all the transactions eBay must handle on a daily basis? To make this concrete the last US Census includes details of 304,059,724 people (US Census Bureau) with data on age, gender, ethnicity, household make up, home structure, income, farms, business and sales available. In July 2008 Google found 1 trillion (1,000,000,000,000) unique URLs on the web at once and eBay handles in excess of 1 billion payments per year. While Google and eBay and indeed their customers gain value from the applications on offer, simply storing the raw data after the fact is of little value unless useful high level information and hence knowledge can be derived from it. Many researchers and commercial organisations are facing similar tasks with large amounts of image data, video, geographic data, textual data or statistical data.
However when trying to understand details about millions of customers, webpages or products the amount of raw data makes the analysis task difficult. One approach to the problem is to convert the data into pictures and models that can be graphically displayed. The intuition behind the use of such graphics is that human beings are inherently skilled at understanding data in visual forms. We refer to the use of computer graphics to visually represent and convey the meaning of abstract information "Information Visualisation".
This talk will outline how various types of information is modeled, managed, mined and hence visually presented on screen for exploration. Several large scale data and information visualisation methods will be described and discussed along with the 7 key challenges we face as researchers and developers in using visualisation in an attempt to present information. These 7 key challenges are: Empowerment, Connection, Volume, Hetrogeneity, Audience, Dynamism and Discovery.
Labels: clique, CSET, graph drawing, hci, humanities, infovis, research, visualisation