Signal processing with kdb+

6 Sep 2018 | , ,
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In the latest in our ongoing series of kdb+ technical white papers published on the Kx Developer’s site, Kx engineer Callum Biggs examines how kdb+/q can be used instead of popular software-based signal processing solutions. Signal processing is used for analyzing observable events, such as IoT sensor data, sounds, images and other types of pulses or occurrences.

Callum’s paper explores how statistical signal processing operations (those which assume that signals are stochastic), can be implemented natively within q to remove noise, extract useful information, and quickly identify anomalies. This integration allows for kdb+/q to be used as a single platform for the capture, processing, analysis and storage of large volumes of sensor data.

This paper shows that native applications of signal processing, which historically have been the realm of libraries accessed through Python or C++, can be natively integrated into kdb+ data systems.

You can read the full paper here.


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, […]