Sensor Analytics

Improve products, services, and reliability Sensor_Analytics_IconManufacturers, miners, utilities and fleet operators collect and analyze data across a spectrum of sources, sampling frequencies, volumes and relevance. Sensor, laboratory, maintenance and utilization data forms a rich ecosystem of orthogonal and mineable analytical opportunities, with the potential to drive improvements in product and service quality, on-time delivery, asset maintenance and utilization and employee safety. Industrial data is ordered and highly dimensional – far more complex than the flat, fact-oriented data that conventional databases were designed for. SciDB’s massively scalable windowing functions and complex analytics exploit this inherent structure. Uniquely, SciDB provides both ACID guarantees and full DBMS functionality with an in-database analytics environment that eliminates time consuming data extraction overhead for engineers and analysts. SciDB’s shared-nothing, massively parallel processing (MPP) distributed database runs on 10s to 1000s of commodity-hardware nodes in a cloud or on-premise. Robust array archiving ensures that laboratory and maintenance records that need to be updated with corrections or additions are never overwritten — critical functionality for pharmaceutical and other highly regulated manufacturers and service providers.


Innovative products faster to market Insurance_IconProperty and casualty insurers are developing innovative personalized products and value-added services. These products and services leverage new data sources – from telematics devices in cars that measure driver behavior and engine conditions to GPS devices that collect location data. Trip-based insurance pricing will integrate current road, traffic and weather conditions, drivers’ trip profiles, fuel pricing and road accident history, enabling insurers to offer consumers flexible and attractively priced products. SciDB’s native array model is designed to deal with the massive volumes of time, location, and sensor data collected at high frequency sampling rates, generated by millions of sensors and mobile devices. Scalable complex analytics lets analysts and data scientists build pricing and risk models entirely in-database. SciDB enables true ad hoc data exploration, helping insurers surface new market opportunities and pricing inefficiencies.


Find. Keep. Grow. E_Commerce_IconAs businesses compete to find, keep, and grow customer relationships, the race is on to identify behaviors and traits that predispose customers to accept an offer or click on an ad. E-commerce businesses depend on micro-personalization to make the right offer, at the right time, to the right customer; and on fraud detection to prevent unauthorized transactions. The underlying mathematical algorithms for these use cases are based on linear algebra techniques (e.g. principal component analysis, singular value decomposition, clustering, general linear models) applied to big data. SciDB has an unfair advantage for these applications because it can run these math functions directly in the database on massive datasets spread across a cluster—without moving the data. SciDB’s array data model is the natural way to store sparse multi-dimensional data for these underlying analytic techniques because it makes for fast ad hoc data exploration and stores the data in the format needed for these complex mathematical techniques. Faster data exploration, and advanced complex math on big data means better targeting, more appropriate offers, and more revenue.