What types of data are suitable for Visplore?

Visplore is optimized for data from time-dependent measurements. Typical examples include:

Data model

Visplore assumes data to be structured as a table (or "data matrix"), as it is common in statistics, data bases, and spreadsheet programs like Excel.

Rows of this data table represent data records. Each data record has the same structure, like a set of conducted measurements (as columns, see below). They are often characterized by a point in time (e.g. the "time stamp") or other key information for unique identification.
Columns of the data table are the data attributes, given for each row. For instance, the physical quantities measured by different sensors at each point in time (see example below). Data attributes may be:


The examples below show how typical data tables can look like for import into Visplore:

Example 1: Meteorological time series - with equal step size of 10 min

Example 2: Log of alerts - irregular time stamps

Example 3: Dataset of 4 wind turbines in long format - the same time stamp occurs in many rows

See this use case tutorial on how to work with tables in long format.

Further aspects

Most types of visualization show a few data attributes at the same time, for example a scatter plot of two measured quantities. The default is to visualize all data records. However, it is possible to select or filter subsets of records, for example corresponding to certain time periods. Selecting subsets of records is the primary mechanism of linking multiple views on the data in Visplore.

Visplore supports missing values. It is thus ok if some data attributes are not specified for several records.

Furthermore, Visplore is not limited to time-oriented data. It is ok to load data that does not have any time stamps! Visplore can thus be used to, for example, analyze large sets of objects such as products, artefacts, etc. Such data could look like the following example of cars:


You can only import one data table at a time into Visplore (with up to millions of rows and hundreds of columns).

The following scenarios are common examples requiring pre-processing outside Visplore:

CSV files

For loading data from CSV files, some additional aspects need to be considered:

In Example 3, the ENGIE La Haute Borne windfarm dataset is used for the screenshot.
Source of Dataset (in its original form): https://opendata-renewables.engie.com/
License: Open License version 2.0 published by Etalab: https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf