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IBM Visual Table Analyzer

A tool that allow users to visually interact with, analyze, and reveal patterns in a large data set.


Date Posted: November 13, 2007
Overview Requirements DownloadFAQsForum Reviews

1. How is IBM® Visual Table Analyzer useful to me?
2. Which Visualizations are supported by Visual Table Analyzer?
3. What is Fanlens?
4. What is tabular data and which data formats are supported by Visual Table Analyzer?
5. How do I load the data?
6. How do I specify the tree hierarchy?
7. How do I map the visual features?
8. How do I explore the data?


1. How is IBM® Visual Table Analyzer useful to me?

Visual Table Analyzer is a suite of tools that allow user to visualize and manipulate the tabular data directly. If you want to understand the data categories and distribution in a large-sized table, you can use Visual Table Analyzer and it might help you on deriving insights with its various visualization forms and interactions.
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2. Which Visualizations are supported by Visual Table Analyzer?

Currently, we support a Fanlens visualization especially designed for the tabular data analysis. We will frequently update the package and add more visualizations in future.
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3. What is Fanlens?

Fanlens is an enhancement to the conventional radial, space-filling technique for pie charts. It contains various interactions of incremental layout, distortion-based zooming, picking, and brushing. It is fit for the data with multiple levels of categories.
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4. What is tabular data and which data formats are supported by Visual Table Analyzer?

Tabular data is usually a matrix-like structure of columns and rows containing data cells and is one of the most popular formats for storing data from many different domains. Typical tabular data includes scientific experiment results, sports statistics, business reports, etc. Visual Table Analyzer supports the plain-text table format called comma-separated values (CSV), in which items are separated by blanks or separators.
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5. How do I load the data?

Use Menu > File > Load to open the data-loading dialog box. Click on the Browse button to launch the Open dialog box. The supported file format is .CSV.
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6. How do I specify the tree hierarchy?

Hierarchy specification is performed by defining the sequence of breaking down the data by its columns. After the data is loaded, it is shown in column-row format in the table. Users can add one column into the break-down sequence by checking the column name. The last-checked column will be automatically appended to the former-checked columns. In addition, users can change the order by dragging the columns.
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7. How do I map the visual features?

Visual presentation mapping means using graphical vocabulary, such as color, texture, shape and position, to represent the underlying cell value of the tabular data. In Visual Table Analyzer, we provide two mapping schemes from two separate dimensions of data to slice angle and slice color. Visual presentation can be specified by selecting the column name from the list.
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8. How do I explore the data?

At start-up, Visual Table Analyzer will display the highest two levels of the hierarchy that presents the summary information. These levels are regarded as the base levels that can be changed using the plus (+) and minus (-) buttons. Users could also explore one branch of the hierarchy by clicking on the slices to expand or collapse the branch, level by level.

Picking is automatically performed when mouse is over the slice. The picked node will be highlighted and, for a very thin slice, it will be enlarged to ensure that the user has a clear view.

Visual Table Analyzer also supports Zoom to deal with the thin-slice problem and is implemented by enlarging the sweep angle of the focus so that all the thin slices in it are enlarged as well. Users can drag the border of the focus area or directly control the slider bar at the bottom to set the sweep angle.


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View screenshots:
An example of the Fanlens enhancement for visualizing the tabular data.

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Related technologies

For platform(s):
Java

For topics:
analysis, Data Analysis, data management, data mining, Java technology, utilities, visualization


 

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