Big Data Analysis
Big Data’s true value is realized not just when all of its content is made searchable but when all of its content is analyzed and new insights are made discoverable. Traditional Big Data practice focuses on making the information searchable. If you know specifically what you are looking for, these search capabilities can help you identify specific documents, database records, or other content that mention the terms of interest. NetOwl, on the other hand, focuses on discovery to derive new insights and make better decisions. In particular, NetOwl offers unique capabilities of discovery from the unstructured data side of Big Data, including Social Media, News, Web, Email, and various message traffic feeds.
Long before “Big Data” became the buzzword it is today, NetOwl was deployed in Big Data environments to help discover critical information, and it continues to do so. NetOwl, for example, helps its customers discover key insights associated with both known and previously unknown persons, places, organizations, and other entities as well as relationships among them. NetOwl also helps match and resolve those entities across disparate data sources to help assemble a complete picture of all the information associated with these entities.
NetOwl’s capabilities are deployable in a variety of distributed computing environments to support Big Data, including Hadoop. Hadoop provides a scalable framework through its MapReduce processing paradigm where NetOwl analyzes large quantities of unstructured data to extract key entities, relationships, events, and geospatial data. This NetOwl-derived semantic information can be further exploited by downstream analytical and visualization software by storing it into NoSQL databases such as Accumulo, HBase, Cassandra, Amazon Dynamo DB, and Mongo DB, XML databases such as MarkLogic, distributed search platforms such as Lucene-based Elasticsearch and Solr, or traditional RDBMS, to support more advanced Big Data analysis.