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How GlaxoSmithKline Uses Kinetica to Manage Their R&D Information Platform

By Richard West | July 19, 2017

Mark Ramsey, SVP, R&D Data, at GlaxoSmithKline, spoke at a recent IBM/Kinetica executive breakfast, where he discussed how GSK uses GPUs and Kinetica to help transform the way that data is used as a strategic asset within their R&D organization.   GlaxoSmithKline (GSK) is a science-led global healthcare company that researches and develops a broad…

Interactive Spatial Analysis with Massive Datasets

By James Dilworth | July 12, 2017

As more and more data becomes available from sensors, from customers, from transactions—much of it with time and location information—there are increasing demands to analyze these data sets and visualize the results on maps. But today’s geospatial toolsets are hardly up to the task. Spatial databases weren’t designed for a world where IoT systems might…

Five examples where GPU databases are bringing ‘real-time’ to IoT analytics

By Michele Nemschoff | July 7, 2017

If your organization manages large volumes of streaming IoT data, you’ll no doubt be familiar with some of the challenges of getting value and insight from these high volume, moving datasets. Many companies have turned to Hadoop and other open source technologies to store and manage these IoT data feeds. This may typically involve “duct…

Kinetica Secures Series A Investment of $50 Million

By Amit Vij & Nima Negahban | June 29, 2017

Today we’re pleased to announce that Kinetica has closed a $50M Series A funding round. With the increased investment, we seek to further expand our solution capabilities while broadening the adoption of Kinetica – a next-generation database platform for analytics and AI accelerated by GPUs. Kinetica remains uniquely positioned to support global enterprises as they…

Bring Your Own Compute (BYOC) – How In-database Analytics on the GPU Unlocks the Potential of Machine Intelligence in Finance

By Karthik Lalithraj | June 21, 2017

Compute analytics in financial services have evolved over the past decade. Popular tools for forecasting and assessing risk like statistical functions involving linear regression, logistic regression have given way to more sophisticated models. Classic decision tree or regression tree algorithms have evolved to modern variations like random forest techniques and gradient boosted trees. Fraud detection…