Panasonic, one of the largest home electronics product manufactures in the world, has launched a number of IoT home appliances connected to the cloud (air conditioners, rice cookers, microwave ovens, etc.). Along with providing cloud services to these products, Panasonic collects operation and state logs from the connected home appliances.
Takayuki Nishikawa and Ei Yamaguhi offer an overview of Panasonic’s recently developed integrated data analytics platform, which uses Hadoop and Hive as core components with NTT DATA (one of the largest system integrators in Japan, which is widely contributing to Hadoop and Spark community with several committers) in order to analyze the ever-increasing log data from these IoT home appliances. The platform makes the complicated data analytics process simple and has achieved scalability for millions of households and yielded an over 10x improvement in processing time. The platform also analyzes and extracts valuable information from multiappliance log data using machine learning with Spark MLlib, enabling Panasonic to get more reliable knowledge about users’ lifestyles.
Takayuki and Ei discuss the technical know-how obtained from the use case, designing and building the platform architecture, migrating existing processes, and constructing an analysis model with Spark MLlib.
Takayuki Nishikawa is a data scientist at Panasonic Corporation working on developing a big data analytics platform to analyze IoT home appliances logs for the purpose of designing new products or functions, creating new services, maintaining appliances, and so on. In addition, Takayuki shares his knowledge about data analysis using machine learning in the company. His academic background is in artificial intelligence, and he holds a master’s degree in computer science from Osaka University in Japan. In his free time, Takayuki enjoys DIY electronics as a Maker.
Ei Yamaguchi is a system infrastructure engineer and leads the OSS professional service team at NTT DATA Corporation, where he is responsible for introducing Hadoop, Spark, and other OSS middleware into enterprise systems. Previously, Ei led system development in the financial industry and developed an enterprise Hadoop cluster and machine-learning applications.
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