Navigating the Data Cosmos with Polaris: A Transformative Data Exploration Tool

Let us dive into the world of Polaris, a transformative system that simplifies the exploration of extensive multi-dimensional databases. It stands as a beacon guiding users through the complexities of data analysis and visualization, making it an indispensable tool for researchers and data analysts. In 2020, Tableau co-founders Chris Stolte and Pat Hanrahan, with their former colleague Diane Tang, won the Test of Time Award for the research underlying Tableau — a paper titled Polaris: a system for query, analysis and visualization of multi-dimensional relational databases.

The Power of Polaris

Polaris is a powerful tool designed for interactive exploration of complex, multi-dimensional relational databases. It breaks down data into manageable tables, where rows represent basic entities, and columns encapsulate their properties. This simplification of data helps users grasp the essence of their data quickly. What sets Polaris apart is its ability to categorize data fields into two fundamental types: ordinal and quantitative. This categorization simplifies data organization, with nominal fields treated as dimensions and quantitative fields as measures. With this structure in place, users can create data visualizations that cater to their specific needs, transforming data analysis into a streamlined process. One of Polaris’s key strengths is its ability to handle data-dense displays, a common challenge when dealing with multi-dimensional data. Whether you’re discovering correlations between variables, identifying patterns, locating outliers, or uncovering hidden structures in your data, Polaris provides multiple display types tailored to each analytical task. Its exploratory interface allows you to pivot and explore data in unpredictable ways, adapting to your evolving questions and insights.

Crafting Visualizations with Polaris

At the heart of Polaris lies the concept of a “visual specification.” This specification consists of three core components:

Table Configurations: When working with Polaris, users create structured tables for their data. The system offers an algebraic approach to defining these configurations, where you can specify the layout of rows, columns, and layers. The magic happens when you assign ordinal and quantitative fields to these axes, effectively creating the framework for your data visualization.

Types of Graphics: Polaris provides a rich set of graphic types organized into three families: ordinal-ordinal, ordinal-quantitative, and quantitative-quantitative. Each family is designed to address specific analytical needs, allowing you to choose the most suitable graphic type to represent your data effectively. From tables to bar charts, scatterplots, and more, Polaris has you covered.

Visual Mappings: Each record in a visualization is mapped to a visual mark, and Polaris offers flexibility in this mapping process. You can choose the visual properties like shape, size, orientation, color, and even texture. While the system can generate default mappings, you have the freedom to fine-tune these mappings to align with your data representation goals.

The Three Phases of Polaris Data Flow

Polaris orchestrates the journey from raw data in the database to the final visualization through a three-phase data flow. These phases are precisely described using SQL queries, making the entire process structured and efficient.

Step 1: Selecting the Records

The first phase involves selecting records from the database while applying user-defined filters to choose data subsets. These filters are expressed using SQL predicates, tailored for both ordinal and quantitative fields. The goal here is to extract the data relevant to your analysis.

Step 2: Partitioning the Records into Panes

In the second phase, Polaris partitions the selected records into groups, aligning with the layout of the visualization, whether it’s rows, columns, or layers. The structure of the partitions is determined by normalized set expressions, with each entry defining a unique group. This step is crucial in organizing data for effective visualization.

Step 3: Transforming Records within the Panes

The final phase focuses on transforming records within each pane. If your visual specification includes aggregation, each measure in the database schema is assigned an aggregation operator. Aggregate field filters are applied at this stage. The resulting SQL statement performs grouping, filtering, and sorting to create the final visualization. If there are no aggregate fields, the transformation simply sorts the records according to specified criteria.

A Transformative Approach to Data Interaction

In the realm of data analysis and visualization, Polaris represents a transformative approach. It doesn’t treat users as passive observers but as active directors of their data exploration journey. This tool places the power in the hands of analysts, allowing them to guide, manipulate, and visualize their queries in real-time.

In Conclusion

In the ever-expanding data universe, Polaris is the North Star that leads the way, making data analysis not only efficient but also an exciting adventure. It’s a tool that unlocks the potential of data, turning it into a story waiting to be told by the analysts who dare to explore.

C. Stolte, D. Tang and P. Hanrahan, “Polaris: a system for query, analysis, and visualization of multidimensional relational databases,” in IEEE Transactions on Visualization and Computer Graphics, vol. 8, no. 1, pp. 52–65, Jan.-March 2002, doi: 10.1109/2945.981851.