Kdb+ Use Case: Machine Learning Water System Maintenance Application

6 Dec 2017 | , ,
Share on:

By Abby Gruen

 

Kdb+ is being used much more widely in machine learning applications today. Its ability to quickly ingest and process data, particularly large, fragmented datasets, is one way that developers are adding kdb+ to their technology stack of artificial intelligence and machine learning tools.

For Australian kdb+ developer Sherief Khorshid, who also develops machine learning systems, incorporating kdb+ into a predictive maintenance application gave him the edge in a hackathon win that landed him a cash prize and a contract with the Water Corporation of Western Australia.

Background

The Ministry of Data in Perth is on a mission to promote an innovation ecosystem in Western Australia. To that end, it has started sponsoring hackathons where the brightest technologists and start-ups in the state take on some of the toughest problems facing Western Australian government agencies.

The first hackathon was held over a weekend in August 2017. Dozens of engineers gathered in a competition to solve problems posed by two government agencies: Main Roads, which oversees road access, and the Water Corporation, which manages water, wastewater and drainage.

A team led by Khorshid tackled the Water Corporation problem, which asked developers to come up with a way that the Water Corporation could identify events and triggers of potential problems in the water system early enough to reduce asset failure and enable timely maintenance within the network.

The challenge

The Water Corporation gave the hackers 20 years of performance history from three water plants. There were about 1,000 physical files for each plant. The data consisted of over 300 columns and 1.5 million rows. Over the years, the format of the data had changed several times, so it was extremely messy and inconsistent. Although the data was technically considered  “computer output,” it was not really computerized in the sense that it was not in a single format that could be simply loaded into a database and analyzed as is.

The solution

Up against a ticking clock, Khorshid chose to use kdb+ rather than Python to ingest and pre-process the data because he knew he could save himself many hours in the process. He quickly saw that he would need to use kdb+ bulk pattern matching to extract the data into consistent data representations in CSV format ready to re-ingest and analyze.

Khorshid’s team used a number of neural network and XGBoost regression models to make real-time forecasts of asset failure.  His team also created a web based GUI connecting directly to a  kdb+ instance, allowing for two-way communication and data transfer.

The application that Khorshid built had a kdb+ process powering the back-end, “pumping out” the historical water network data. The back-end pushed out one hours’ worth of data every second. In the final presentation at the hackathon, Khorshid was able to show that multiple clients from around the world were able to simultaneously run the app, and query the data.

The results

Khorshid, who originally learned kdb+ as a developer at an investment bank, found that kdb+ was the best tool for pre-processing the water plant data. “When doing a lot of data munging, it is a lot easier in kdb+ than in Python,” Khorshid said.

The value of converting previously inaccessible data into usable datasets can be a significant business benefit for organizations like the Water Corporation. The agency has been collecting SCADA data for two decades from thousands of pumps and pipes, and then simply storing the data away in an historical database that they have never been able to utilize before.

Once cleansed and normalized, that data can be incorporated into improved applications for detecting leaks; measuring the effectiveness of the water system; for demand forecasting, or for preventative maintenance, all of which can meaningfully improve water operations and their service to their customers.

SUGGESTED ARTICLES

Kx extends relationship with NASA Frontier Development Lab and the SETI Institute

Detection of Exoplanets at NASA FDL with kdb+

13 Dec 2018 | , , , ,

Kx data scientist Espe Aguilera explains a NASA FDL mission to improve the accuracy of finding new exoplanets using machine learning models. The data for the project will come from the Transiting Exoplanet Survey Satellite (TESS), which was launched in April 2018, with the objective of discovering new exoplanets in orbit around the brightest stars in the solar neighborhood.

Kx extends relationship with NASA Frontier Development Lab and the SETI Institute

The Exploration of Space Weather at NASA FDL with kdb+

4 Dec 2018 | , , , ,

Our society is dependent on GNSS services for navigation in everyday life, so it is critically important to know when signal disruptions might occur. Physical models have struggled to predict astronomic scintillation events. One method for making predictions is to use machine learning (ML) techniques. This article describes how kdb+ and embedPy were used in the ML application.

kdb+ Machine Learning embedPy

Machine learning: Using embedPy to apply LASSO regression

23 Oct 2018 | , , ,

By Samantha Gallagher   The use of kdb+ for machine learning in financial technology and other industries is expanding following the release by Kx of the powerful embedPy interface, which allows the kdb+ interpreter to manipulate Python objects, call Python functions, and load Python libraries. Now Python and kdb+ developers can fuse both technologies together, […]