This is the first entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the changing data landscape and realizing a scalable data lake, as well as offerings from HPE for big data analytics. The first entry is focused on the recent exponential growth of data.
Given their easy accessibility, many researchers use article abstracts to identify a collection of articles for use in text mining. But, while abstracts provide some valuable pieces of information, there are major advantages to taking steps using and mining full-text articles instead. Read on as Michael Iarrobino, Product Manager at Copyright Clearance Center, explains the advantages of mining full-text articles over abstracts.
This is the sixth and final article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll provide a series of “best practices” and “lessons learned” for what companies are seeking from deploying IoT analytics.
The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey, “insideHPC / insideBIGDATA AI/Deep Learning Survey 2016,” to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.
In this contributed article, Harry Glaser, CEO of Periscope Data, discusses the newly announced knock-out platform from Uber called “Movement,” which will offer access to its data around traffic flow in scores where it operates, intended for use by city planners and researchers looking to improve mobility.
In this special technology white paper, From Small to Big Data, Adopting the Advanced Analytics Mindset, you’ll learn how to help data teams — analysts, scientists, and managers — to collaborate on data projects. One of the key success factors for these teams is to allow analysts to work on Big Data as easily as they do on smaller data with Excel, as well as to help them find new use cases specific to the data available and the tools at hand.
In this whitepaper, you’ll learn how advanced analytics has the potential to transform the ways in which segmentation for marketing purposes is accomplished. It starts with a look at traditional segmentation methods and then moves on to exploring how advanced analytics (model-based segmentation) can change the game. Then you’ll explore a few marketing & analytics use cases in various industries. Lastly, you’ll examine the methodologies needed to implement model based segmentation in the real world.
In this special technology white paper, The 5 Key Challenges to Building a Successful Data Science Lab & Data Team, you’ll learn how a Data Lab establishes an effort to answer business needs by making sense of raw information. Data labs are intended to create critical mass within the organization that enables them to reach the level of innovation required for new data-driven products.
This is the third article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll provide an overview of the rise of IoT analytics. IoT Analytics implies data, fast data, and big data. IoT is not just about capturing sensor data, or GPS locations, or temperature, or velocity changes. You have to find meaning in that data through analytics.
In this special technology white paper, From Development to Production Guide – Finding the Common Ground in 9 Steps, you’ll learn how managing a successful data science project requires time, effort, and a great deal of planning. Defining the problems to solve and planning the project’s scope is just the tip of the iceberg, as team members need to fully understand all aspects of a project in order to effectively contribute.