Reaction: TileBars: Visualization of Term Distribution Information in Full Text Information Access

I liked the start of this paper which stresses upon the need to analyse the data retrieved by a query. The idea suggested here gives more though on the structure and properties of the data retrieved by a query. This is in particular to the length, frequency and other properties of the word. Thus the tool suggested in this paper by the author, “TileBar” promises to provide an efficient solution for this.

I particularly liked the structure of the paper presented. The author has presented the standard information retrieval techniques. In particular there is a discussion on the “similarity search” method for understanding the similarity between the information retrieved from multiple document sources. The author points out some of the drawbacks of using the similarity search techniques like ranking, information placing and the way information is discussed throughout the text. There is also a difference when the comparison is made on the full text and the abstract. Thus as a solution for the problems discussed, the method of TileBars is presented.

Tilebars provides a good visual inference for the data retrieved by a query. In particular I liked how the length of the bars varies as per the document length. It also gives a good graphical representation with the change in brightness for keeping a track on the frequency of the terms. The method seems to prove quite useful in the example medical scenarios presented in this paper. Though a discussion on few more instances or examples could have proven the usability of this tool more.

This is the second work presented by Marti A. Hearst that has been reviewed in this set. I could state that the author gives lot of importance upon the information visualisation and understanding the visual presented. Even “TileBar” presented in this paper is used for the same purpose.