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Dynamic Aggregation to Support Pattern Discovery: A Case Study with Web Logs (2001)

dc.contributor.authorTang, Lidaen_US
dc.contributor.authorShneiderman, Benen_US
dc.description.abstractRapid growth of digital data collections is overwhelming the capabilities of humans to comprehend them without aid. The extraction of useful data from large raw data sets is something that humans do poorly because of the overwhelming amount of information. Aggregation is a technique that extracts important aspect from groups of data thus reducing the amount that the user has to deal with at one time, thereby enabling them to discover patterns, outliers, gaps, and clusters. Previous mechanisms for interactive exploration with aggregated data was either too complex to use or too limited in scope. This paper proposes a new technique for dynamic aggregation that can combine with dynamic queries to support most of the tasks involved in data manipulation.en_US
dc.format.extent6646483 bytes
dc.relation.ispartofseriesISR; TR 2005-24en_US
dc.titleDynamic Aggregation to Support Pattern Discovery: A Case Study with Web Logs (2001)en_US
dc.typeTechnical Reporten_US

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