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In data and information visualization, the goal is to graphically present and explore abstract, non-physical and non-spatial data collected from databases, information systems, file systems, documents, business data, etc. (presentational and exploratory visualization) which is different from the field of scientific visualization, where the goal ...
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Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
Brushing and linking is also an important technique in interactive visual analysis, a method for performing visual exploration and analysis of large, structured data sets. [2] Specifically, linking consists of a change of parameters (for example a data filter) in one data representation being reflected in other connected data representations.
Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Tulip aims to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems being addressed.
Business intelligence software is a type of application software designed to retrieve, analyze, transform and report data for business intelligence (BI). The applications generally read data that has been previously stored, often - though not necessarily - in a data warehouse or data mart .
DADiSP is designed to perform technical data analysis in a spreadsheet like environment. However, unlike a typical business spreadsheet that operates on a table of cells each of which contain single scalar values, a DADiSP Worksheet consists of multiple interrelated windows where each window contains an entire series or multi-column matrix.
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."