View From the Top by Nima Negahban Cofounder and CEO Kinetica
Data with a time and space component is proliferating. Prime examples are streams of data from mobile devices, static or moving sensors, satellites, and video feeds from drones and closed-circuit TVs. The first generation of IoT data were readings over time. The current generation of IoT data are readings over time and space. Understanding this trend and the resulting impacts are essential for innovators seeking to create value in the next wave of IoT products and services.
Conventional analytic databases were designed to analyze transactions and first generation Big Data like web logs. But, getting value from sensor data characterized by time-stamps and geo-encoding requires new capabilities that aren’t satisfactorily addressed by prior generation databases, even those with special object-relational extensions for spatiotemporal data.
Kinetica’s design center is fusing massive geospatial and time-series data sets together and processing complex spatio-temporal analytics in real-time. Kinetica uses native vectorization to significantly outperform other cloud analytic databases. In a vectorized query engine, data is stored in fixed-size blocks called vectors, and query operations are performed on these vectors in parallel, rather than on individual data elements. This allows the query engine to process multiple data elements simultaneously, resulting in faster query execution and improved performance, particularly those that require fusion of temporal and spatial data in real-time.
Kinetica is powering new location-driven solutions for Liberty Mutual, TD Bank, the NBA, Lockheed Martin, T-Mobile, FAA, Ford and many others.
To learn more, visit: https://www.kinetica.com/