Industrial Analytics with Juxt

Machine generated manufacturing data is growing exponentially in volume, variety and velocity. This creates a huge opportunity for manufacturing houses to increase operational efficiency, improve productivity and gain a competitive advantage. Equipment data is generated at every level in different formats and granularities. This data is eminently suited for storage and analysis using cutting edge technology.

However, a vast majority of the data is still not used due to the lack of the very specialized technical expertise imposed by today’s tools. Juxt makes it easy for non-technical process experts to unearth the value of their operational data.

Juxt is a data and process integration system that enables industrial houses to leverage predictive analytics in the areas of  preventive/prescriptive maintenance, yield optimization and operational efficiency.

Challenges to a Successful Data Driven Project

A number of companies hesitate to invest a lot of dollars and time upfront to implement data driven operations while they are unsure of the returns.

Companies are concerned about having to build an in-house team of technical experts after the initial implementation for maintenance and enhancements.

Companies that do invest in data projects end up finding that their implementations are a ‘black-box’ environment unable to incorporate new learnings or evolving requirements

Juxt Benefits

Modular – Juxt offers a highly modular approach to building data apps. This enables companies to start simple and extend the functionality in phases as the value becomes apparent. This approach also enables users to quickly respond to changing requirements as they gain insights from their data

‘No Code’ Visual Designer – Juxt mitigates the need for a large team of technical experts using a easy to use visual, drag, drop and configure UX. With Juxt, process experts put together a process workflow using functional Lego(c)  blocks, tie them together and hit run. Integrate data from a variety of sources, run predictive machine learning algorithms,visualize the results and drive operational responses.

Distributed Deployment – Juxt is highly scalable and can be deployed across the hierarchy. Analytics can be deployed at the edge where the data is generated, in the cloud or a hybrid of the two. Juxt has a very small footprint with deployment options in embedded systems at the edge close to the sensors enabling real time analytics right at the source.  At the same time, Juxt is big data scalable with connectors to Hadoop data stores and compute clusters. juxt industrial automation system

Juxt Technical Services

Juxt offers technical consulting services to kickstart your data projects. Please contact to chat.


Analyzing the Movielens Data – Part 3

This is Part 3 of our “Analyzing the Movielens data” series.

In Part 2, we answered the following by building Juxt flows:

  • What is the average rating for each movie broken down by gender?
  •  What are the top 10 movies that men rate higher than women?

Continuing on, let’s address the next one

  • List only the good movies – the ones that got an average rating of 4.3 or higher

In the process of doing this, we’ll go over how to build custom filters using Select building block.

The logical steps to address this question are

  • Calculate the average rating for every movie title (total aggregate, not broken down by gender)
  • Select(filter) only the movies that meet the 4.3 cut-off.
Juxt Flow - Movies with Ratings > 4.3
Juxt Flow – Movies with Ratings > 4.3

As before, we start with fetching the data from the user DB with Fetch from User DB.

Average rating per title can be calculated using the built-in Rollup library module (Recall that we had used a Pivot Table in the last example to further break it down by gender, but we have a simpler problem here).

The Rollup module outputs just two parameters – Title (Group by parameter) and Mean-Rating (aggregated feature).

Now, we need a mechanism to go over each of the entries and make a comparison against our selection criteria – mean > 4.30.

We use Select module for that. The Select module takes in each entry row by row and applies the user specified filter logic. We have a simple logic here, but you can apply rather sophisticated logic with multiple parameters using this mechanism.

In addition to the input data, Select module has two other inputs. Context Parameters enables users to provide extra parameters needed for the logic and a Drop down menu for picking the filter.
In our example, we use the filter called good movie selector.

Selector Logic – Juxt uses key-value stores. We use Lookup module with a key of mean-rating to a comparator block If True which compares the mean rating value with the preset value from Context Parameters which in this case is the number 4.3.

Juxt Flow - Selector Logic to Filter Movies > 4.3
Juxt Flow – Selector Logic to Filter Movies > 4.3

Finally, we render the results as a HTML Data Table

Results - Movies with Ratings > 4.3
Results – Movies with Ratings > 4.3

A two minute video of our discussion can be seen here