A Hyperconnected Manufacturing Collaboration System Using the Semantic Web and Hadoop Ecosystem System
Abstract With the explosive growth of digital data communications in synergistic operating networks and cloud computing service, hyperconnected manufacturing collaboration systems face the challenges of extracting, processing, and analyzing data from multiple distributed web sources. Although semantic web technologies provide the solution to web data interoperability by storing the semantic web standard in relational databases for processing and analyzing of web accessible heterogeneous digital data, web data storage and retrieval via the predefined schema of relational SQL databases has become increasingly inefficient with the advent of big data. In response to this problem, the Hadoop Ecosystem System is being adopted to reduce the complexity of moving data to and from the big data cloud platform. This paper proposes a novel approach in a set of the Hadoop tools for information integration and interoperability across hyperconnected manufacturing collaboration systems. In the Hadoop approach, data is " E xtracted" from the web sources, " L oaded" into a set of the NoSQL Hadoop Database (HBase) tables, and then " T ransformed" and integrated into the desired format model with Hive's schema on read. A case study was conducted to illustrate that the Hadoop Extract Load Transform (ELT) approach for the syntax and semantics web data integration could be adopted across the global smartphone value chain.