Tree visualization with tree-maps: 2-d space-filling approach
ACM Transactions on Graphics (TOG)
Visual information seeking: tight coupling of dynamic query filters with starfield displays
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
FOCUS: the interactive table for product comparison and selection
Proceedings of the 9th annual ACM symposium on User interface software and technology
Structured graph format: XML metadata for describing Web site structure
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Interface and data architecture for query preview in networked information systems
ACM Transactions on Information Systems (TOIS)
AVI '00 Proceedings of the working conference on Advanced visual interfaces
Multidimensional Database Technology
Computer
Query, analysis, and visualization of hierarchically structured data using Polaris
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A visual interface technique for exploring OLAP data with coordinated dimension hierarchies
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Building a display of missing information in a data sieve
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
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Multi-dimensional data occurs in many domains while a wide variety of text based and visual interfaces for querying such data exists. But many of these interfaces are not applicable to OLAP, as they do not support the use of dimension hierarchies for selection and aggregation. We introduce an interface technique which supports visual querying of OLAP data. It is based on a data graph rather than a data cube representation of the data. Our interface presents each dimension hierarchy in a zoomable panel which supports selection and aggregation at multiple levels. Users explore data and query by making selections in several dimension views. Three view coordinations are identified; progressive, global and result only. We demonstrate our interface technique with an example web log dataset of site visits organised into time, downloads, visitor address and referrer address dimensions. This article provides an extended treatment of an earlier short paper [6].