Learning CascadingLearning Cascading

We are pleased to announce the release of our new book, Learning Cascading, published by Packt Publishing. Find our more about it on our publisher’s website.

Build reliable, robust, and high performance big data applications using the Cascading application development framework efficiently! Our book is available from Packt Publishers, Amazon, and many other book stores.






Whitepapers, Presentations, and Webinars:

You can download our whitepapers and presentations, and view our webinars  here.

new Reducing Hospital Re-admissions with Big Data Predicative Analytics webinar

As part of Concurrent’s educational webinar series, Concurrent asked Michael Covert, our CEO, a Big Data analytics expert, to share his experiences in the Healthcare field. In this webinar, Michael examined how Healthcare Providers are finding ways to use Big Data analytics to reduce readmission rates and improve operational efficiency while complying with regulatory mandates. He discussed the following topics:

  • Creating a deep learning architecture for sophisticated predictive analytics
  • Implementation considerations for a working predictive analytics solution: MedPredict™
  • Best practices for building, monitoring and managing your Big Data analytics applications

new Big Data Text Analytics

Text Analytics (TA) (which is a broader term for NLP, text mining, computational linguistics, information retrieval and information extraction) is the art and science of automatically or semi-automatically extracting meaningful information from unstructured text. With the exponential growth of unstructured text data (social media is one of the culprits here) Big Data technologies are imperative for the efficient and speedy text analytics tasks. In fact Big Data technologies infuse enhanced machine learning capabilities into TextAnalytics.

Big Data Advanced Analytics

New big data frameworks have now emerged that make software development easier, and also provide insulation from rapidly evolving infrastructure changes. The infrastructure itself is evolving quickly and more standardized platforms and compute fabrics have increased system stability and performance. New software frameworks are being released and revised and they continue to advance the capabilities of big data development initiatives allowing sophisticated delivery of more complex forms of analytics. Predictive and prescriptive analytics have surfaced as the new driving concept in big data. Systems today use machine learning, graph theory, natural language processing, and business rules to discover, predict, and prescribe actions. A new area, referred to as Deep Learning is now beginning to supplant Machine Learning.

In this article we review the aforementioned big data advanced analytics concepts. We also provide an overview and discussion of big data development frameworks and how they are used for expedient design and development of advanced analytics systems. Finally we discuss how the systems based on these concepts are utilized by different industries for discovery and deep learning.

Reducing Hospital Readmission Expense using Cascading™
Hospital readmission is an event that health care providers are attempting to reduce, and it is a primary target of new regulation from the US Affordable Care Act. A readmission is defined as ANY reentry to a hospital 30 days or less from a prior discharge. A financial impact is that US Medicare and Medicaid will either not pay or will reduce the payment made to hospitals for expenses incurred. By the end of 2014, over 2600 hospitals will incur these losses from a Medicare and Medicaid tab that is thought to exceed $24B annually.

An Introduction to Relationship Mining
In this paper, we discuss how organizations can analyze their internal data sources to provide knowledge about the inner workings of their employees, customers, vendors, and other entities that have “interesting relationships” with each other. We will discuss what “interesting” means, how it can be determined, and what knowledge and meaning it imparts. We will also discuss some specific examples of how this can be applied to certain business problems.

From Machine Learning to Deep Learning
In this paper we will discuss concepts of Machine Learning and how it is evolving into a new, more powerful set of concepts called Deep Learning. We will discuss the role of analyzing massive amounts of data using Big Data systems. We will take a look at several cases that are currently being used by businesses to exploit these technologies to produce better financial results, medical outcomes, to reduce crime, and more. We will work through analogy, and we will occasionally discuss some mathematical and statistical principles that we will need to better understand what is occurring. We will conclude with a discussion of where we are headed, from a technological and societal perspective, as these systems evolve and increase in power and applicability.

You can download our whitepapers and presentations, and view our webinars here.

Other Presentations