Frameworks for Distributed Big Data Processing
A Comparison in the Domain of Predictive Maintenance
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Predictive maintenance is a novel approach for making maintenance decisions, lowering maintenance costs, increasing a plants capacity and production volume, and positively affecting environmental and employee safety. In predictive maintenance, condition data of machines is constantly collected and analysed to predict future machine failures. Due to the high volume, velocity, and variety of gathered data, Big Data analytic frameworks are necessary to provide the desired results. The performance of these frameworks highly influences the overall performance of a predictive maintenance system, rai...
Predictive maintenance is a novel approach for making maintenance decisions, lowering maintenance costs, increasing a plants capacity and production volume, and positively affecting environmental and employee safety. In predictive maintenance, condition data of machines is constantly collected and analysed to predict future machine failures. Due to the high volume, velocity, and variety of gathered data, Big Data analytic frameworks are necessary to provide the desired results. The performance of these frameworks highly influences the overall performance of a predictive maintenance system, raising the need for tools to measure it. Benchmarks present such tools by defining general workloads for a system to measure its performance. Due to the wide popularity of Big Data analytics across industries, benchmarks for Big Data analytic frameworks are defined specifically for each domain. While there are currently many benchmarks available for other domains such as retail, social network, or search engines, there are none available for Big Data analytic frameworks in the application area of predictive maintenance. This book introduces the predictive maintenance benchmark (PMB).
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