Kasper Hornbæk
Abstract
We present an exploration of reading patterns and usability in visualizations of electronic documents. Twenty subjects wrote essays and answered questions about scientific documents using an overview+detail, a fisheye, and a linear interface. We study reading patterns by progression maps that visualize the progression of subjects' reading activity, and by visibility maps that show for how long different parts of the document are visible. The reading patterns help explain differences in usability between the interfaces and show how interfaces affect the way subjects read. With the overview+detail interface, subjects get higher grades for their essays. All but one of the subjects prefer this interface. With the fisheye interface, subjects use more time on gaining an overview of the document and less time on reading the details. Thus, they read the documents faster, but display lower incidental learning. We also show how subjects only briefly have visible the parts of the document that are not initially readable in the fisheye interface, even though they express a lack of trust in the algorithm underlying the fisheye interface. When answering questions, the overview is used for jumping directly to answers in the document and to already-visited parts of the document. However, subjects are slower at answering questions with the overview+detail interface. From the visualizations of the reading activity, we find that subjects using the overview+detail interface often explore the document further even when a satisfactory answer to the given question has already been read. Thus, overviews may grab subjects' attention and possibly distract them.
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Index Terms
- Reading patterns and usability in visualizations of electronic documents
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Reviews
Evangelia Kavakli
Nearest neighbor searches are important in many database and classification applications. Sequential searches are too expensive to perform and take too long for efficient interactive query activity. The data is usually multidimensional, and efficient indexing for searching may become difficult to construct. The clustering with singular value decomposition (CSVD) method described in this paper allows the user to reduce the dimensionality of the search space, limit the loss of information, and achieve very good performance. The description of the method is well organized and thorough. After a brief mathematical introduction to singular value decomposition (SVD) and indexing methods for nearest neighbor searching, CSVD is presented in detail, reporting all of the steps involved in index construction and similarity searching. The paper concludes with a set of computational experiments on three data sets, two of which were synthetic and one that was real. The numerical experiments compared the performance of CSVD against simple SVD and local dimensionality reduction (LDR), which was reviewed in an appendix. LDR was the reference method. CSVD outperformed SVD significantly, and was comparable to LDR on the synthetic data. On real data, CSVD was more efficient than LDR. Online Computing Reviews Service
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