Search

Three months of server transaction logs containing complete clickstream data for an image collection digital library were analysed for usage patterns to better understand user searching and browsing behaviour in this environment. Eleven types of user actions were identified from the log content. The study is novel in its combined analytical techniques and use of clickstream data from an image-based digital library. Three analytical techniques were used to analyse the data: (a) network analysis to better understand the relationship between sequential actions; (b) sequential pattern mining to identify frequent action sequences; and (c) k-means cluster analysis to identify groups of session patterns. The analysis revealed strong ties between several pairs of actions, relatively short pattern sequences that frequently duplicate previous actions and largely uniform session behaviour with little individual item browsing within sessions, indicating users are primarily engaged in purposeful and directed searching. Developers of image-based digital libraries should consider design features that support rapid browsing. [ABSTRACT FROM AUTHOR]

Presents a citation analysis of selected mass communication journals using clustering and multidimensional scaling techniques. Relationships among international journals; US journals at the center of the field; Description of the field through geographic and theory/research dimensions; Scholarly community development; Ties among journals within a given discipline or subfield.

The influence of sample size on informetric characteristics is examined to determine whether theoretical mathematical models can adequately fit large data sets. Two large data sets of queries submitted to the Excite search service were sampled for search characteristics (term frequencies, terms used per query, pages viewed per query, queries submitted per session) producing data sets of various sizes that were fitted to theoretical models to determine how the sample may influence a model's goodness-of-fit. Although theoretical models could adequately fit smaller data sets of up to 5000 observations in some cases, larger data sets could not be satisfactorily fitted using several goodness-of-fit techniques. Investigators must take into account that sample size does influence goodness-of-fit outcomes. The nature of the data and not the limitations of given goodness-of-fit tests results in significant outcomes. Such goodness-of-fit tests should be used for comparative purposes, rather than significance testing. [ABSTRACT FROM AUTHOR]