| Summary: | With the flourishing of Internet of Things (IoT) technology, ubiquitous power data can be linked to the Internet and be analyzed for real-time monitoring requirements. Numerous power data would be accumulated to even Tera-byte level as the time goes. To approach a real-time power monitoring platform on them, an efficient and novel implementation techniques has been developed and formed to be the kernel material of this thesis. Based on the integration of multiple software subsystems in a layered manner, the proposed power-monitoring platform has been established and is composed of Ubuntu (as operating system), Hadoop (as storage subsystem), Hive (as data warehouse), and the Spark MLlib (as data analytics) from bottom to top. The generic power-data source is provided by the so-called smart meters equipped inside factories located in an enterprise practically. The data collection and storage are handled by the Hadoop subsystem and the data ingestion to Hive data warehouse is conducted by the Spark unit. On the aspect of system verification, under single-record query, these software modules: HiveQL and Impala SQL had been tested in terms of query-response efficiency. And for the performance exploration on the full-table query function. The relevant experiments have been conducted on the same software modules as well. The kernel contributions of this research work can be highlighted by two parts: the details of building an efficient real-time power-monitoring platform, and the relevant query-response efficiency for reference.
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