https://eprints.ncl.ac.uk/file_store/production/228275/D8E53094-D19C-4E63-8DDC-1A8732719703.pdf

Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations

Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.

Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges

Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

In this paper, a survey of the literature of the past 15 years involving machine learning (ML) algorithms applied to self-organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of self-organizing networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this paper also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

As data science becomes increasingly mainstream, there will be an ever-growing demand for data science tools that are more accessible, flexible, and scalable. In response to this demand, automated machine learning (AutoML) researchers have begun building systems that automate the process of designing and optimizing machine learning pipelines. In this chapter we present TPOT v0.3, an open source genetic programming-based AutoML system that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification task. We benchmark TPOT on a series of 150 supervised classification tasks and find that it significantly outperforms a basic machine learning analysis in 21 of them, while experiencing minimal degradation in accuracy on 4 of the benchmarks—all without any domain knowledge nor human input. As such, genetic programming-based AutoML systems show considerable promise in the AutoML domain.

/pdf/tpot-a-tree-based-pipeline-optimization-tool-for-automating-2f74avb3h0.pdf

TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning

Too Big to Ignore: The Business Case for Big Data isgeared towards CIOs, CEOs, presidents, and IT professionals. At a high level, the book makes a compelling business case for that which we are calling Big Data. Simon provides commonsense advice for organizations looking to make sense out of the information streaming at us with unprecedented volume, velocity, and variety. Think big.

Too Big to Ignore: The Business Case for Big Data

Phil Simon Q&A with Phil Simon, author of The Visual Organization Why did you write The Visual Organization? Jim Barksdale, former CEO of Netscape, once famously said, If we have data, lets look at data. If all we have are opinions, lets go with mine. I love that quote, and its in the book. Barksdale is probably happy right now. Today more than ever, professionals are being asked to argue their cases and make their decisions based on data. A new, data-oriented mind-set is permeating the business world. Blame or credit Google or Nate Silver. For instance, journalists, drycleaners, and football teams today are representing data in interesting ways, a subject Ive discussed frequently on my blog and with colleagues and clients. Next, I believe that the book fills a vacuum. I reviewed many of the current books on data visualization. While enormously helpful, they tend to be how-to books. As such, they emphasize theory over practice. The title of my book is no accident: I am unaware of an existing text that examines how actual organizations, departments, and people have used contemporary dataviz tools to move the needle. This is particularly true with dataviz. As I discovered researching The Visual Organization, there arent too many original, insightful, and vendor-neutral case studies on the topic. This is a big problem. Far too many business books lack case studiesand suffer as a result. When done right, case studies can be enormously helpful, as they provide real-world business context and valuable lessons. The Visual Organization takes a show me, dont tell me approach. Finally, and this is a purely selfish reason, I enjoy the writing process. At the end of months and months of work, it feels pretty good to hold your book in your hand. Is The Visual Organization similar to your last book, Too Big to Ignore: The Business Case for Big Data? And how does it different from other data visualization books? Theres definitely a bit of overlap, but these are very different books. Too Big to Ignore serves as a what I believe is a useful and jargon-free overview of a very important subject: Big Data. I detail the most prominent technologies, applications, and tools. Among the most important questions that book answers is, How are organizations finding the signal in the noise that is Big Data? Thats a really big question, one that necessitated much more breadth than depth. As such, Too Big to Ignore provides overviews of Hadoop, NoSQL, different statistical methods, natural language processing, data visualization, and other Big Data tools. Many books have beenand are beingwritten about each of those technologies. The Visual Organization is different on two fronts. First, it is much deeper than it is wide. Second, it is unlike existing dataviz books by Nathan Yau, Stephen Few, and Edward Tufte. The Visual Organization is fundamentally about how progressive organizations today are using a wide array of dataviz tools to ask better questions of their dataand make better business decisions. With a data-friendly mind-set, companies like Netflix, Wedgies, eBay, the University of Texas, and Autodesk are garnering amazing insights into their operations, users, customers, products, and employees. You start the book with the story of the Tableau IPO? What did it signify to you? In short, the arrival of The Visual Organization. Think about it. One year after the Facebook IPO bombed, Tableaus stock skyrocketed 63 percent. Here is a company that does one thing: dataviz. Thats it. I found the contrast to the Facebook IPO riveting, not to mention endemic of a much larger trend. You write about the recent proliferation of dataviz tools. Can you elaborate here?IBM Cognos, SAS, and other enterprise BI stalwarts are still around, but they are no longer the only game in town. Today, an organization need not spend hundreds of thousands or millions of dollars to get going with dataviz. These new tools have become progressively more powerful and democratic over the last decade. Long gone are the days in which IT needed to generate reports for non-technical employees. They have made it easier than ever to for employees to quickly discover new things in increasingly large datasets. Examples include Visually, Tableau, Vizify, D3.js, R, and myriad others. Yeah, but dont most organizations already do dataviz? Sure, to some extent. A simple Excel graph or chart certainly qualifies as rudimentary dataviz, but its unlikely to promote true data discovery. Many of the interactive dataviz tools I discuss in the book are far better suited for this critical type of exploration. More generally, many CXOs are paying lip service to Big Dataand the importance of data in general. In my view, though, relatively few are truly harnessing its power. Of course, there are exceptions to this rule. Id include Amazon, Apple, Facebook, Google, Twitter, and Netflix, among others. If you peel back the onion, youll see that employees these organizations are doing a great deal more than creating simple graphs, bar charts, and pivot tables. Employees here are interacting with their data, learning new things about their businesses in the process. Thats a major theme of the book. Any advice on becoming a visual organization? Buy and read the book. All kidding aside, remember the famous words of Peter Drucker: Culture eats strategy for breakfast. Tools matter, but an organizations culture often plays a bigger role in its success. The same is true here. Also, its a marathon, not a sprint. Sure, theres low-hanging fruit. Remember that Google, like Rome, was not built in a day.

The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions

Your all-inclusive guide to all the latest technologies Providing you with a better understanding of the latest technologies, including Cloud Computing, Software as a Service, Service-Oriented Architecture (SOA), Open Source, Mobile Computing, Social Networking, and Business Intelligence, The Next Wave of Technologies: Opportunities from Chaos helps you know which questions to ask when considering if a specific technology is right for your organization. Demystifies powerful but largely misunderstood technologies Explains how each technology works Provides key guidance on determining if a particular technology is right for your organization Contains contributions from experts on Cloud Computing, Service-Oriented Architecture (SOA), Software as a Service (SaaS), Open Source, Mobile Technologies, Enterprise Risk Management, Social Media, Business Intelligence, and more More of a management text than a technical guide, the book is designed to help your organization better understand these exciting new technologies and their potential impact. The Next Wave of Technologies: Opportunities from Chaos will help you determine if your organization is ready for a specific technology, how to prepare for its successful adoption, how to measure success, and the key risks and red flags to recognize.

The Next Wave of Technologies: Opportunities in Chaos

Demystifying Big Data

A Fortune 500 manufacturing company spent millions attempting to implement a new enterprise resource planning (ERP) system. Across the globe, a 150-employee marketing firm built and tried to implement a proprietary customer relationship management (CRM) system. For two very different companies doing two very different things, the outcomes were identical. In each case, the organization failed to activate and utilize its system as initially conceived by senior management. And these two organizations are hardly alone. On the contrary, research indicates that more than three in five new IT projects fail. Many miss their deadlines. Others exceed their initial budgets, often by ghastly amounts. Even systems activated on time and under budget often fail to produce their expected results and almost immediately experience major problems. Although the statistics are grim, there is at least some good news: these failures can be averted. Organizations often lack the necessary framework to minimize the chance of system failure before, during, and after beginning IT projects. Why New Systems Fail provides such a framework, with specific tools, tips, and insight from the perspective of a seasoned, independent consultant with more than a decade of related experience. The book examines in great detail the root causes of system failures. Detailed case studies, examples, and lessons from actual system implementations are presented in an informative, straightforward, and very readable manner. More than a theoretical or technical text, this book offers pragmatic advice for organizations both deploying new systems and maintaining existing ones.

Phil Simon

Papers

Too Big to Ignore: The Business Case for Big Data

The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions

The Next Wave of Technologies: Opportunities in Chaos

Demystifying Big Data

Why new systems fail : an insider's guide to successful IT projects