A framework for vacuuming temporal databases
A framework for vacuuming temporal databases
DIVE-ON: from databases to virtual reality
Crossroads
XmdvTool: visual interactive data exploration and trend discovery of high-dimensional data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases
IEEE Transactions on Visualization and Computer Graphics
Information Retrieval from an Incomplete Data Cube
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Modelling Multidimensional Data in a Dataflow-Based Visual Data Analysis Environment
CAiSE '99 Proceedings of the 11th International Conference on Advanced Information Systems Engineering
Query, analysis, and visualization of hierarchically structured data using Polaris
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiscale Visualization Using Data Cubes
IEEE Transactions on Visualization and Computer Graphics
Multiple Foci Drill-Down through Tuple and Attribute Aggregation Polyarchies in Tabular Data
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Incomplete information in multidimensional databases
Multidimensional databases
Advanced visualization for OLAP
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
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
OLAP over uncertain and imprecise data
The VLDB Journal — The International Journal on Very Large Data Bases
SUBJECT: a directory driven system for organizing and accessing large statistical databases
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
A tour through the visualization zoo
Communications of the ACM
Exploring web logs with coordinated OLAP dimension hierarchies
DNIS'05 Proceedings of the 4th international conference on Databases in Networked Information Systems
DOLAP 2011: overview of the 14th international workshop on data warehousing and olap
Proceedings of the 20th ACM international conference on Information and knowledge management
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A data sieve filters a data stream to harvest data of interest and summarizes the harvested data in a multidimensional database (MDB). To build the data sieve, a designer supplies a list of filters. Each filter consists of a filter unit and category for each dimension. The filter unit specifies a pattern (a regular expression) to match as the data stream is filtered. The filter category is the system of measurement in which occurrences of that pattern are counted or otherwise aggregated. Since filtering discards some of the data, incomplete regions within the MDB are created. The missing data complicates querying. While a query on the filtered data can be automatically analysed to determine if sufficient information has been filtered to satisfy it, a better query construction strategy is to prevent users from formulating unsatisfiable queries. To aid users in formulating only satisfiable queries, the GUI for a data sieve needs to color or otherwise display regions of complete, partially complete, and missing data. As a user constructs a query, choosing categories and units, the displayed incomplete regions shift and change, curtailing future choices. For instance, if a user selects a spatial unit of Australia, the display for a temporal category of days may need to be colored as incomplete since no filters would satisfy both selections. We describe an algorithm that uses bit strings to create and maintain the display of incomplete information in a data sieve in real-time.