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.

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.

Kx Insights: Machine learning and the value of historical data

2 Aug 2018 | , , ,

Data is being generated at a faster rate now than ever before. IDC has predicted that in 2025, there will be 163 zettabytes of data generated each year—a massive increase from the 16.1 zettabytes created in 2016. These high rates of data generation are partially an outcome of the multitude of sensors found on Internet of Things (IoT) devices, the majority of which are capable of recording data many times per second. IHS estimates that the number of IoT devices in use will increase from 15.4 billion devices in 2015 to 75.4 billion in 2025, indicating that these immense rates of data generation will continue to grow even higher in the years to come.