This paper talks about TileBars which is a visualization technique to display information and relation between full text of multiple documents. It lets user to show relative length of document, frequency of terms and distribution of terms across the document(also with respect to other documents).
Paper talks about older techniques for information retrieval just work with title and abstracts. Paper tells about most common approach for text retrieval where it informs about similarity search. Similarity search uses vector space model and probabilistic model for determining how closes document are with each other. In this it uses Boolean retrieval where documents are extracted in ranking order after they satisfy constrain given by user. This way suffered from numerous drawbacks so they proposed TileBars.
TileBars helps user in decision of which documents to view but it goes in more detail by telling which passage of those documents. TileBars displays search result of information retrieval using Tiles in square. Color shades of tiles is used to visualized term frequency and their size is used to visualize length of documents. This is achieved using ‘TextTiling’. Paper’s discussion about ‘TextTiling’ algorithm is very short but paper provides brief about its working. TextTiling provides boundaries between subtopic using term repetition in documents.
I agree to the author’s points that columns of TileBars can easily be searched and understood in comparison to previously stated techniques. Overall I find paper very well structured but somewhat hard to understand for some sections. Even after being old I think it is still can also be used today with news or information website (like blogs). Also I do feel some similarity(use of tile and color) between this visualization and Map of the Market which we discussed in class.
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