Dynamic Aggregation to Support Pattern Discovery: A case study with web logs

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dc.contributor.authorTang, Lidaen_US
dc.contributor.authorShneiderman, Benen_US
dc.date.accessioned2004-05-31T23:17:03Z
dc.date.available2004-05-31T23:17:03Z
dc.date.created2002-03en_US
dc.date.issued2003-01-21en_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. (UMIACS-TR-2002-26) (HCIL-TR-2001-27)en_US
dc.format.extent318703 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/1188
dc.language.isoen_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4345en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2002-26en_US
dc.relation.ispartofseriesHCIL-TR-2001-27en_US
dc.titleDynamic Aggregation to Support Pattern Discovery: A case study with web logsen_US
dc.typeTechnical Reporten_US

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