Hadoop Meets Semantic Technology: Data Scientists Win
Hadoop is on almost every enterprise’s radar – even if they’re not yet actively engaged with the platform and its advantages for Big Data efforts. Analyst firm IDC earlier this year said the market for software related to the Hadoop and MapReduce programming frameworks for large-scale data analysis will have a compound annual growth rate of more than sixty percent between 2011 and 2016, rising from $77 million to more than $812 million.
Yet, challenges remain to leveraging all the possibilities of Hadoop, an Apache Software Foundation open source project, especially as it relates to empowering the data scientist. Hadoop is composed of two sub-projects: HDFS, a distributed file system built on a cluster of commodity hardware so that data stored in any node can be shared across all the servers, and the MapReduce framework for processing the data stored in those files.
Semantic technology can help solve many of the challenges, Michael A. Lang Jr., VP, Director of Ontology Engineering Services at Revelytix, Inc., told an audience gathered at the Semantic Technology & Business Conference in New York City yesterday.



Richard Cyganiak, one of the Recommendation’s editors, explained why R2RML is so important. “In the early days of the Semantic Web effort, we’ve tried to convert the whole world to RDF and OWL. This clearly hasn’t worked. Most data lives in entrenched non-RDF systems, and that’s not likely to change.”
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Underway at the W3C are some major Semantic Web efforts that can have a big impact on the enterprise.
Eric Franzon
VP Community
Jennifer Zaino
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