Browse Prior Art Database

Data Table Scrubber

IP.com Disclosure Number: IPCOM000248914D
Publication Date: 2017-Jan-22
Document File: 5 page(s) / 777K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a data table scrubber tool and method to constantly provide context of placement to the user when performing a search, executing a find, and/or browsing within wide data sets.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

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Data Table Scrubber

Users who are navigating through a large data set can feel lost or confused about the current “location” within the data set. Current interfaces focused on big data can be confusing and cluttered with too many user interface (UI) elements.

As a navigation method, standard scrolling functionality is too basic; it does not highlight the keyword entities within the data set. With table filters and finders, search fields and query builders are robust tools, but may hide column-to-column relationships while the user attempts to browse through wide data sets. A browser finder does not work with data sets.

The novel solution is data table scrubber system comprised of a method to constantly provide context of placement to the user when performing a search, executing a find, and/or browsing within wide data sets.

The core idea is to allow users to search and navigate wide data, while providing context of placement within the data set. This allows the user to explore the data set with the context of the current location on the table. The user can search for terms on the data set, in which the results can be displayed in context of the rest of the table or only showing the columns/cells that relate. The user can navigate the data set without feeling lost or frustrated. It helps the user visualize the size and build of the data set. In addition, the user can have a zoom-out view and easily explore the data set.

The advantage over the current technology is context. Current technologies do not provide context and hide column-to-column relationships while trying to browse through wide data sets.

The following example embodiment illustrates the components and process for implementing the data table scrubber.

Scenario: User A is a business analyst. User A pulls a data set into a shaper to clean and explore the data, scanning it to be sure that it has the needed data. User A is interested in any data that is associated with the term, “name”.

1. To open the find/filter tray (scrubber) User A clicks the magnifying glass in the tool bar.

Figure 1: Default view, scrubber closed

2. The scrubber opens (pushes the table downward), which allows User A to visually see the size of the data set. Each column is represented by one tic mark.

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Figure 2: Scrubber opened. The tics highlighted in a magenta color represent those corresponding columns in view, and the grey tics represent columns that are not in view.

3. User A can enter a term on the top left of the scrubber and can see on the top right of the scrubber, a summary of the columns in the current view

Figure 3: Scrubber opened, showing summary of columns in view

4. When User A hovers over a column on the table, the corresponding tic on the scrubber changes color (e.g., blue). The scrubber and the table correspond to each other in all interactions.

5. User A selects a column on the table. The size of the tic...