Seeq applications
Seeq Corporation, a manufacturing and Industrial Internet of Things (IIoT) advanced analytics software company, has announced the availability of its latest release, R22, and beta availability of Seeq Data Lab, at the ARC Industry Forum 2020.
The company’s applications include Workbench for advanced analytics, Organizer for publishing insights in reports and dashboards, and Data Lab for accessing Python libraries. These applications allow engineers and scientists in process manufacturing organisations to analyse, predict, collaborate and share insights to improve operations and business outcomes.
New R22 features support enterprise data governance initiatives and priorities to support Seeq expansion in large organisations. For example, Seeq Integrated Security honours OSIsoft PI security restrictions for PI data access and enables administrators to set signal-level permissions on data in historians and other time series data stores. Each user therefore only has access to the data they need, facilitating data access compliance.
Additional R22 features include:
- Improved Scatterplot in Workbench: conditional filtering and more display options help users find relationships among signals more quickly
- NOAA Weather Service Connector: data from the National Weather Service API can be easily integrated into Seeq analytics
- Multi-user Awareness: now easy to know when other users are viewing, editing, or presenting the same Worksheet or Topic
Data Lab is built on Jupyter Notebooks and a Seeq Python library, called Spy, to access Seeq functionality, and it is managed by the same administration features as other Seeq applications.
All of the browser-based Seeq applications – Organizer, Workbench, and Data Lab – connect to a shared server to enable collaboration, access connected data sources, and enable administrative control. For example, graphics created in Data Lab may be viewed in Organizer Topics, data modelled in Workbench may be accessed by Data Lab users and Data Lab algorithm results are available for use in the other Seeq applications.
Example use cases for process engineers and data scientists using Seeq Data Lab include:
- Advanced models: Python libraries for inclusion of Neural Network, Random Forest, K-means, and other algorithms
- Custom visualisations: Python libraries for the display of specific data types and calculations can be incorporated in Data Lab analytics
- Non time-series data: Python libraries enable analytics which include population statistics, data look-ups, and multi-dimensional data