If you’re building or growing a data science team, the first reflex is to hire new talent. Before you do so, take a few moments to ask yourself the following questions.
One major challenge when converting PDFs to full-text for mining is diminished data integrity. The conversion process can introduce errors (e.g., poor character recognition for uncommon fonts) and often removes tags that indicate sections of the article, such as introduction, conclusion, and materials and methods, making the corpus difficult to mine.
Text mining enables the rapid review and analysis of large volumes of biomedical literature, giving life science companies valuable insights to drive research and development and inform business decisions. For example, the results of mining projects can provide a greater understanding of the underlying biology behind specific diseases and how they respond to certain drugs, and support the target discovery process.
In biomedical research and development, researchers use text mining tools to extract and interpret facts, assertions, and relationships from vast amounts of published information. Mining accelerates the research process, increases discovery of novel findings, and helps companies identify potential safety issues in the drug development process. However, despite the many benefits of text mining, researchers face a number of obstacles before they even get a chance to run queries against the body of biomedical literature.
This Introduction to SPARK webinar will feature Daniel Gutierrez, Managing Editor of insideBIGDATA.
In the past year, the Apache Spark distributed computing architecture has continued its upward trajectory amongst the big data players. Its growth has been fueled by several innovative differentiators for big data applications, such as MapReduce 2.0 (or YARN), provisions for analytic workflows, and efficient use of memory. Databricks’ recent 2015 Spark industry survey reports that Spark adoption is outpacing Hadoop because of its accelerated access to big data. In support of this new computing architecture.
Converging High Performance Computing (HPC) and Lustre* parallel file systems with Hadoop’s MapReduce for Big Data analytics can eliminate the need for Hadoop’s infrastructure and speeding up the entire analysis. Convergence is a solution of interest for companies with HPC already in their infrastructure, such as the financial services Industry and other industries adopting high performance data analytics.
The pace at which the world creates data will never be this slow again. And much of this new data we’re creating is unstructured, textual data. Emails. Word documents. News articles. Blogs. Reviews. Research reports… Understanding what’s in this text – and what isn’t, and what matters – is critical to an organization’s ability to understand the environments in which it operates. Its competitors. Its customers. Its weaknesses and its opportunities.
A number of industries rely on high-performance computing (HPC) clusters to process massive amounts of data. As these same organizations explore the value of Big Data analytics based on Hadoop, they are realizing the value of converging Hadoop and HPC onto the same cluster rather than scaling out an entirely new Hadoop infrastructure.
Building out a Hadoop cluster with massive amounts of local storage is a considerably extensive and expensive undertaking, especially when the data already resides in a POSIX compliant Lustre file system. Now companies can adopt analytics written for Hadoop and run them on their HPC clusters.
With the release of Intel® Cloud Edition for Lustre software in collaboration with key cloud infrastructure providers like Amazon Web Services (AWS), commercial customers have an ideal opportunity to employ a production-ready version of Lustre—optimized for business HPDA—in a pay-as-you-go cloud environment.