Altair® Panopticon


[1] Working with Altair Panopticon Real Time with a Designer Role


Altair Panopticon™ supports web authoring wherein, a user with a Designer role can assemble, maintain, and publish monitoring and analysis workbooks in the Web client.



Visual Data Discovery is performed through workbooks. A workbook is a collection of:

q Dashboards (Visual Layouts)

q Data Tables (Data Query and Schema Definitions)

q Actions (Contextual Interaction Definitions)

q   Overall styling


Dashboards may consist of several parts including: visualizations, legends, filters, action controls, labels, and images.

Data tables output both data schemas and data conduits, and define the queries and source data repository definitions, to retrieve data. They do not store data but are simply the conduit to which data flows through.

The core of the product is the processing of data, which can range from Real Time Streaming data sets, that are retrieved asynchronously, to static and historical datasets and are retrieved synchronously on a defined periodic basis. It is assumed that data is never at rest, and consequently, data refresh is an automatic operation across all data sets.

Data sources can be connected to directly, with data retrieved on the fly as it is required.


Data can be accessed in several methods, depending on the need and source repositories capabilities:

q  Retrieve all data into memory.

For example, retrieving an MS Excel spreadsheet.

q  Retrieve subsets into memory, which may be summarized, or parameterized.

For example, retrieving a summary view, and then retrieving a detailed dataset, based on the selection in the summary view. This method provides very tightly controlled data retrieval times but requires the paths through data to be pre-specified, with pre-defined data queries (including stored procedures).

 Retrieve only required results into memory, by querying on demand, pushing aggregation and filtering tasks to underlying big data repositories, or queryable data stores.

This is commonly known as a ROLAP implementation, where the product is dynamically writing data queries to the underlying data repository and retrieving aggregated and filtered datasets. Given the on-demand nature of this method, it is more suitable to exploratory data analysis but requires dynamic query generation.

In the following sections the product will be demonstrated, starting with the various layouts, the definition of data retrieval and then the building of dashboards. Other topics include working with webhooks, setting up alerts, and configuring workbook themes.






Version 2023.2/12.2023