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Identifying Health Risks Using Pattern Recognition and AI

Physicians are increasingly using AI technologies to treat patients with superhuman speed and performance, and predictive analytics will be key to delivering more effective, proactive, and quality care. Stephen Wheat, Director of HPC Pursuits at Hewlett Packard Enterprise, explores how we can identify health risks using pattern recognition and AI. 

Big Data Law Enforcement and the Rise of Predictive Policing

In this contributed article, freelance human Avery Phillips discusses how big data technology coupled with predictive analytics combine to constitute a new area of law enforcement – predictive policing. While the tension around predictive policing’s ethical validity remains, results seem to be positive enough in preventing crime for it to become a more commonly used method of policing.

Interview: Vipin Kumar, Regents Professor and William Norris Chair in Large Scale Computing at University of Minnesota

The following is a discussion with Vipin Kumar, Regents Professor and William Norris Chair in Large Scale Computing at University of Minnesota; ACM Fellow 2015. The Association of Computing Machinery (ACM) just concluded a celebration of 50 years of the ACM A.M. Turing Award (commonly known as the “Nobel Prize of computing”) with a two-day conference in San Francisco. The conference brought together some of the brightest minds in computing to explore how computing has evolved and where the field is headed.

Reach Analytics Adds Automated Response Modeling Capabilities to Its Self-Service Predictive Marketing Platform

Reach Analytics, a leading predictive platform for marketing teams, announced the launch of new-to-market automated response modeling capabilities. The new features are now live and available for use by marketers to increase campaign response rates.

From PMML to PFA: A Way Forward for Deploying Predictive Analytics

In this contributed article, Jason Miller, Industrial Applications Engineer at Alpine Data discusses the new PFA standard that takes a good step forward from the previous PMML standard. A shift to PFA has the potential to be a watershed event in predictive analytics.

InsideBIGDATA Guide to Predictive Analytics

Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Learn more by downloading this Guide to Predictive Analytics.

Interview: Ankur Goyal, VP of Engineering at MemSQL

I recently caught up with Ankur Goyal, Vice President of Engineering at MemSQL, to get an inside look at the new era of predictive analytics. Ankur runs engineering at MemSQL. He was one of the first employees at MemSQL starting in 2011, and has actively driven growth of the team since then.

Text Analytics without Tradeoffs

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.

Predictive Modeling and Production Deployment

Using predictive analytics involves understanding and preparing the data, defining the predictive model, and following the predictive process. Predictive models can assume many shapes and sizes, depending on their complexity and the application for which they are designed. The first step is to understand what questions you are trying to answer for your organization.

Data Access and Exploratory Data Analysis

Enterprise data assets are what feed the predictive analytic process, and any tool must facilitate easy integration with all the different types data sources required to answer critical business questions. Robust predictive analytics needs to access analytical and relational databases, OLAP cubes, flat files, and enterprise applications.