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Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases Presented by Darren Gates for ICS 280 Introduction • Polaris is a system for exploring large multi-dimensional databases, using the Pivot Table interface, but extending this idea to graphical displays and allowing the construction of complex queries. • Polaris uses tables to organize multiple graphs on a display, with each table consisting of layers and panes. Pivot Tables • Multi-dimensional databases are often treated as n-dimensional cubes. • Pivot Tables allow rotation of multidimensional datasets, allowing different dimensions to assume the rows and columns of the table, with the remaining dimensions being aggregated within the table. Example: Baseball data • By dragging and dropping the dimensions to and from the left-hand column, top row, upper-left corner, and central data area (where the remaining dimensions are aggregated), one can change the Pivot Table view. Any of these views can be subsequently graphed. Polaris Design Concepts 1 • An analysis tool for a large, multidimensional database must: – allow data-dense displays for a large number of records and dimensions – allow multiple display types – have an exploratory interface; should be able to rapidly change how data is viewed Polaris Design Concepts 2 • Characteristics of tables that make them effective to display multi-dimensional data: – multivariate: multiple dimensions can be encoded in the structure of the table – comparative: tables generate “small-multiple” displays of information – familiar: users are accustomed to tabular displays Polaris Display 1 • Drag and drop fields from database scheme onto shelves • May combine multiple data sources, each data source mapping to a separate layer • Multiple fields may be dragged onto each shelf • Data may be grouped or sorted, and aggregations may be computed Polaris Display 2 • Selecting a single mark in a graphic displays the values for the mark • Can lasso a set of marks to brush records • Marks in the graphics use retinal properties (see subsequent slide) Table Algebra • A formal mechanism to specify table configurations • Operators: – concatenation + – cross x – nest / Graphics • Ordinal-Ordinal: e.g. the table – the axis variables are typically independent of each other • Ordinal-Quantitative: e.g. bar chart – the quantitative variable is often dependent on the ordinal variable • Quantitative-Quantitative: e.g. maps – view distribution of data as a function of one or both variables; discover causal relationships Retinal Properties • Ordinal/nominal mapping vs. quantitative mapping • Properties: Shape, size, orientation, and color. • When encoding a quantitative variables, should only vary one aspect at a time Querying • Three steps: – Select the records – Partition the records into panes – Transform the records within the panes • To create database queries, it is necessary to generate an SQL query per table pane (i.e. must iterate over entire table, executing SQL for each pane). Discussion • Allows overlap between the relations that are divided into each pane of the Polaris display, unlike the basic Pivot Table model. • Allows more versatile computation of aggregates (e.g., medians and averages, in addition to sums). • Intuitive drag-and-drop interface, like that seen in Pivot Tables Possible Improvements • Generate database tables from a selected set of marks • Integrate a table lens, instead of having to click a mark to view its details