Kdb+ Use Case: Machine Learning Water System Maintenance Application

6 Dec 2017 | , , , ,

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.

Using q in Machine Learning with Neural Network and Clustering Examples

4 Apr 2017 | , , , , ,

Tokyo-based kdb+ programmer, and algorithmic quantitative analyst, Mark Lefevre recently gave a couple of talks about using high-performance machine learning with kdb+ at the Kx Community Tokyo Meetup. His talk “Using Q to Read Japanese” focused on utilizing neural networks and how supervised learning can be used in q to teach a machine to recognize Japanese characters from handwritten images. His second talk, “Kx for Wine Tasting” focused on utilizing the k-means clustering algorithm and unsupervised learning in q to teach a machine to appreciate wine!

Business Growth Fund partnership to extend Kx technology in new markets

19 Feb 2017 | , , , , , ,

Kx parent First Derivatives (FD) recently announced its latest strategic partnership is with the Business Growth Fund (BGF), one of the UK’s leading investors. This new alliance plans to seed Kx technology in up-and-coming businesses looking for the kind of technological advantage that only Kx can provide. Target areas will include cyber security, AI, blockchain, robotics, nanotechnology, life sciences, neurology and machine learning.

GitHub: Machine learning project for kdb+/q

14 Dec 2016 | , , ,

Software engineer and kdb+ programmer Juan Lasheras recently added a kdb+/q machine learning project to GitHub. The aim of Juan's ml.q repository is to act as a multi-purpose machine learning toolkit. It provides multiple useful methods that practitioners can use for data analysis and predictive modeling. It is comparable to the scikit-learn toolkit for Python. Check out his repo on GitHub to see the algorithms he implemented.