Polaris A System for Query Analysis and Visualization of Multi dimensional Relational Database by Chris Stolte Pat Hanrahan presenter Andrew Trieu ICS 280 Information Visualization Department ICS at UCI April 18 2002 A Large Multi Dimensional Database A major challenge for these huge databases is to extract meaning from the data they contain such as to discover structure to find patterns and to derive causal relationship Continue The exploratory analysis process is one of hypothesis experiment and discovery The path of exploration is unpredictable and the analysts need to be able to rapidly change both what data they are viewing and how they are viewing that data Pivot Table The most popular interface to multi dimensional databases Allow the data cube to be rotated so that different dimensions of the dataset may be encoded as rows or columns of the table The remaining dimensions are aggregated displayed as numbers in the cells of the table Pivot Table Continue Cross tabulations and summaries are then added to the resulting table of numbers Finally graphs may be generated from the resulting tables A Polaris System Polaris is an interface for the exploration of multi dimensional databases that extends the Pivot Table interface to directly generate a rich expressive set of graphical displays Polaris Continue Polaris builds tables using an algebraic formalism involving the fields of the database Each table consists of layers and panes and each pane may be a different graphic Features of Polaris An interface for constructing visual specifications of table based graphical displays and the ability to generate a precise set of relational queries from the visual specifications The visual specifications can be rapidly incrementally developed giving the users visual feedback as they construct complex queries visualization Features of Polaris con t The state of the interface can be interpret as a visual specification of the analysis task and automatically compile it into data and graphical transformations Users can incrementally construct complex queries receiving visual feedback as they assemble and alter the specifications Related Work to Polaris The related work to Polaris can be divided into three categories formal graphical specifications table based data display and database exploration tools Definition We refer to a row in a relational table as a tuple or record and a column in the table as field The field in a database can be characterized as nominal ordinal or quantitative Definition continue Polaris reduces this categorization to ordinal and quantitative by assigning an ordering to the nominal fields subsequently treating them as ordinal The fields within a relational table can also be partitioned into two types dimensions and measures Polaris treats all nominal fields as dimensions and all quantitative fields as measures Analysis of databases To effectively support the analysis process in large multi dimensional databases an analysis tool must meet several demands Data dense displays Multiple display types Exploratory interface Data dense displays Analysts need to be able to create visualizations that will simultaneously display many dimensions of large subsets of the data Multiple display types Analysis consists of many different task such as discovering correlation between variables finding patterns in the data locating outliers and uncovering structure An analysis tool must be able to generate displays suited to each of these tasks Exploratory interface The analysis process is often an unpredictable exploration of the data Analysts must be able to rapidly change what data they are viewing and how they are viewing that data Polaris addresses these demands by providing an interface for rapidly and incrementally generating table based displays A table consists of a number of rows columns and layers Each table axis may contain multiple nested dimensions Each table entry or pane contains a set of records that are visually encoded as a set of marks to create a graphic Displaying multi dimensional data Several characteristics to tables make them particularly effective for displaying multi dimensional data Multivariate Comparative Familiar Multivariate multiple dimensions of the data can be explicitly encoded in the structure of the table enabling the display of high dimensional data Comparative tables generate small multiple displays of information which are easily compared exposing patterns and trends across dimensions of the data Familiar Statisticians are accustomed to using tabular displays of graphs such as scatterplot matrices and Trellis displays for analysis Pivot Tables are a common interface to large data warehouses Polaris User Interface Generating Graphics The visual specification consists of three components Table Algebra the specification of the different table configurations Types of Graphics the type of graphic inside each pane Visual Mapping the details of the visual encoding Table Algebra A complete table configuration consists of three separate expressions Two of the expressions define the x and y axes of the table partitioning the table into rows and columns The third expression defines the z axis of the table which partitions the display into layers Table Algebra continue A valid expression in the algebra is an ordered sequence of one or more symbols with operators between each pair of adjacent symbols The operators in the algebra are cross x nest and concatenation listed in order of precedence Table Algebra continue Concatenation operator performs an ordered union of the sets of the two symbols Cross operator performs a Cartesian product of the sets of the two symbols Nest operator is similar to the cross operator but it only creates set entries for which there exist records with those domain values Types of Graphics Polaris allows analysts to flexibly construct graphics by specifying the individual components of the graphics Polaris has structured the space of graphics into three families by the type of field assigned to their axes Ordinal Ordinal Ordinal Quantitative Quantitative Quantitative Ordinal Ordinal Graphic The characteristic member of this family is the table either of numbers or marks encoding attributes of the source records The axis variables are typically independent of each other and the task is focused on understanding patterns and trends Ordinal Ordinal Graphic Ordinal Quantitative Graphic The characteristic member of this family is the bar