- Table View
- List View
Data Center Networks: Topologies, Architectures and Fault-Tolerance Characteristics (SpringerBriefs in Computer Science)
by Yang Liu Jogesh K. Muppala Malathi Veeraraghavan Dong Lin Mounir HamdiThis SpringerBrief presents a survey of data center network designs and topologies and compares several properties in order to highlight their advantages and disadvantages. The brief also explores several routing protocols designed for these topologies and compares the basic algorithms to establish connections, the techniques used to gain better performance, and the mechanisms for fault-tolerance. Readers will be equipped to understand how current research on data center networks enables the design of future architectures that can improve performance and dependability of data centers. This concise brief is designed for researchers and practitioners working on data center networks, comparative topologies, fault tolerance routing, and data center management systems. The context provided and information on future directions will also prove valuable for students interested in these topics.
Data Center Storage: Cost-Effective Strategies, Implementation, and Management
by Hubbert SmithWe overspend on data center storage ... yet, we fall short of business requirements. It's not about the technologies. It's about the proper application of technologies to deliver storage services efficiently and affordably. It's about meeting business requirements dependent on data center storage. Spend less, deliver more. Data Center Storage: Cost-Effective Strategies, Implementation, and Management provides an industry insider's insight on how to properly scope, plan, evaluate, and implement storage technologies to maximize performance, capacity, reliability, and power savings. It provides business and use-case focused coverage of storage technology, including storage area networks (SAN), capacity-optimized drives, and solid-state drives. It offers key insights on financially responsible spending for data center storage. Delivered in accessible language, the book starts with a discussion of the business merits of replacing direct attached, compartmentalized storage with consolidated SAN-attached storage. The author advises on the use of service level applications (SLAs) as a tool to drive business unit collaboration with IT and prioritize those actions that impact productivity and profit from those that are less critical. This business guide to applied technologies disassembles big problems into digestible segments to help you understand, quantify, and fix any problems that arise as you work towards meeting your growing storage needs. The book builds on the consolidation and SLA driven approach to take advantage of the compelling benefits and potential savings of managed hosting and cloud storage.
Data Center Virtualization Certification: Everything you need to achieve 2V0-622 certification – with exam tips and exercises
by Andrea Mauro Paolo ValsecchiDeploy and configure vSphere infrastructure and learn to effectively create and administer vSphere virtual machinesKey FeaturesImplement advanced network virtualization techniquesConfigure and administer vSphere high availabilityEnhance your data center virtualization skills with practice questions and mock testsBook DescriptionThis exam guide enables you to install, configure, and manage the vSphere 6.5 infrastructure in all its components: vCenter Server, ESXi hosts, and virtual machines, while helping you to prepare for the industry standard certification.This data center book will assist you in automating administration tasks and enhancing your environment’s capabilities. You will begin with an introduction to all aspects related to security, networking, and storage in vSphere 6.5. Next, you will learn about resource management and understand how to back up and restore the vSphere 6.5 infrastructure. As you advance, you will also cover troubleshooting, deployment, availability, and virtual machine management. This is followed by two mock tests that will test your knowledge and challenge your understanding of all the topics included in the exam.By the end of this book, you will not only have learned about virtualization and its techniques, but you’ll also be prepared to pass the VCP6.5-DCV (2V0-622) exam.What you will learnDeploy and configure vSphere infrastructureCreate and administer vSphere virtual machinesOptimize, secure, and troubleshoot all vSphere componentsImplement vSphere HA on a vSAN clusterUnderstand how to back up and restore your vSphere 6.5 infrastructureTest your understanding of key concepts required through sample questionsWho this book is forIf you are interested in achieving Data Center Virtualization certification, this is the book is for you. You will also benefit from this book if you are a system administrator or network engineer. Some prior knowledge of virtualization can assist you in understanding key concepts covered in the book.
Data Centric Artificial Intelligence: A Beginner’s Guide (Data-Intensive Research)
by Parikshit N. Mahalle Gitanjali R. Shinde Yashwant S. Ingle Namrata N. WasatkarThis book discusses the best research roadmaps, strategies, and challenges in data-centric approach of artificial intelligence (AI) in various domains. It presents comparative studies of model-centric and data-centric AI. It also highlights different phases in data-centric approach and data-centric principles. The book presents prominent use cases of data-centric AI. It serves as a reference guide for researchers and practitioners in academia and industry.
Data Classification and Incremental Clustering in Data Mining and Machine Learning (EAI/Springer Innovations in Communication and Computing)
by Sk Hafizul Islam Debabrata Samanta Sanjay ChakrabortyThis book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.
Data Classification: Algorithms and Applications (Chapman And Hall/crc Data Mining And Knowledge Discovery Ser. #35)
by Charu C. AggarwalComprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi
Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly
by Michael WalkerExplore supercharged machine learning techniques to take care of your data laundry loadsKey FeaturesLearn how to prepare data for machine learning processesUnderstand which algorithms are based on prediction objectives and the properties of the dataExplore how to interpret and evaluate the results from machine learningBook DescriptionMany individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You'll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you'll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You'll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.By the end of this book, you'll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.What you will learnExplore essential data cleaning and exploration techniques to be used before running the most popular machine learning algorithmsUnderstand how to perform preprocessing and feature selection, and how to set up the data for testing and validationModel continuous targets with supervised learning algorithmsModel binary and multiclass targets with supervised learning algorithmsExecute clustering and dimension reduction with unsupervised learning algorithmsUnderstand how to use regression trees to model a continuous targetWho this book is forThis book is for professional data scientists, particularly those in the first few years of their career, or more experienced analysts who are relatively new to machine learning. Readers should have prior knowledge of concepts in statistics typically taught in an undergraduate introductory course as well as beginner-level experience in manipulating data programmatically.
Data Clustering in C++: An Object-Oriented Approach
by Guojun GanData clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered.This book is divided into three parts-- Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns A C++ Data Clustering Framework: The development of data clustering base classes Data Clustering Algorithms: The implementation of several popular data clustering algorithms A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the downloadable resources. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.
Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series #31)
by Charu C. Aggarwal Chandan K. ReddyResearch on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
Data Collection in Fragile States: Innovations from Africa and Beyond
by Johannes Hoogeveen Utz Pape‘This open access book addresses an urgent issue on which little organized information exists. It reflects experience in Africa but is highly relevant to other fragile states as well.’ —Constantine Michalopoulos, John Hopkins University, USA and former Director of Economic Policy and Co-ordination at the World BankFragile countries face a triple data challenge. Up-to-date information is needed to deal with rapidly changing circumstances and to design adequate responses. Yet, fragile countries are among the most data deprived, while collecting new information in such circumstances is very challenging. This open access book presents innovations in data collection developed with decision makers in fragile countries in mind. Looking at innovations in Africa from mobile phone surveys monitoring the Ebola crisis, to tracking displaced people in Mali, this collection highlights the challenges in data collection researchers face and how they can be overcome.
Data Communication and Networks: Proceedings of GUCON 2019 (Advances in Intelligent Systems and Computing #1049)
by Lakhmi C. Jain George A. Tsihrintzis Valentina E. Balas Dilip Kumar SharmaThis book gathers selected high-quality papers presented at the International Conference on Computing, Power and Communication Technologies 2019 (GUCON 2019), organized by Galgotias University, India, in September 2019. The content is divided into three sections – data mining and big data analysis, communication technologies, and cloud computing and computer networks. In-depth discussions of various issues within these broad areas provide an intriguing and insightful reference guide for researchers, engineers and students alike.
Data Communications Network Auditing
by Bruce GriffisThis book contains product specific information based on Cisco router command line interface, and IBM's Net view. It is designed to help us understand the "parts and pieces" of communications and determine how components fit together, and what they look like on your bill.
Data Communications and Network Technologies
by Huawei Technologies Co., Ltd.This open access book is written according to the examination outline for Huawei HCIA-Routing Switching V2.5 certification, aiming to help readers master the basics of network communications and use Huawei network devices to set up enterprise LANs and WANs, wired networks, and wireless networks, ensure network security for enterprises, and grasp cutting-edge computer network technologies. The content of this book includes: network communication fundamentals, TCP/IP protocol, Huawei VRP operating system, IP addresses and subnetting, static and dynamic routing, Ethernet networking technology, ACL and AAA, network address translation, DHCP server, WLAN, IPv6, WAN PPP and PPPoE protocol, typical networking architecture and design cases of campus networks, SNMP protocol used by network management, operation and maintenance, network time protocol NTP, SND and NFV, programming, and automation. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud-computing, and smart computing to artificial intelligence.
Data Conscience: Algorithmic Siege on our Humanity
by Brandeis Hill MarshallDATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with: Discussions of the importance of transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms
Data Control: Major Challenge for the Digital Society
by Jean-Louis MoninoBusinesses are becoming increasingly aware of the importance of data and information. As such, they are eager to develop ways to "manage" them, to enrich them and take advantage of them. Indeed, the recent explosion of a phenomenal amount of data, and the need to analyze it, brings to the forefront the well-known hierarchical model: "Data, Information, Knowledge". "Data"– this new intangible manna – is produced in real time. It arrives in a continuous stream and comes from a multitude of sources that are generally heterogeneous. This accumulation of data of all kinds is generating new activities designed to analyze these huge amounts of information. It is therefore necessary to adapt and try new approaches, methods, new knowledge and new ways of working. This leads to new properties and new issues as a logical reference must be created and implemented. At the company level, this mass of data is difficult to manage; interpreting it is the predominant challenge.
Data Correcting Approaches in Combinatorial Optimization (SpringerBriefs in Optimization)
by Panos M. Pardalos Boris GoldengorinData Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems. Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.
Data Crush: How the Information Tidal Wave Is Driving New Business Opportunities
by Christopher SurdakThe Internet used to be a tool for telling your customers about your business. Now its real value lies in what it tells you about them. Every move your customers make online can be tracked, catalogued, and analyzed to better understand their preferences and predict their future behavior. And with mobile technology like smartphones, customers are online almost every second of every day. The companies that succeed going forward will be those that learn to leverage this torrent of information--without being drowned by it. Balancing examples from giants like Amazon, Home Depot, and Ford with newer players like Rovio, Groupon, and scores of niche-market winners, Data Crush examines the forces behind the explosive growth in data and reveals how the most innovative companies are responding to this challenge. The book clarifies the key drivers: the proliferation of "big data" generated by a never-ending range of online activities (and the mobility that enables much of it); the seemingly infinite array of digital commerce and entertainment pathways; and the rising growth of Cloud computing. These and other factors combine to create an overwhelming universe of valuable information--all constantly updated in real time with billions of mouse clicks each day. It's daunting, but with this onslaught of information comes tremendous opportunity--and Data Crush will help you make sense of it all.
Data Culture: Develop An Effective Data-Driven Organization
by Dr Shorful IslamOrganizations often start their data journey by either procuring the technology or hiring the people. However, without an effective data-driven culture in place, they can struggle to derive value from their investments.Data Culture explores how data leaders can develop and nurture a data-driven culture tailored to their organization's needs. It outlines the types of data leadership and teams needed and the key building blocks for success, such as team recruitment, building and training, leadership, process, behavioural change management, developing, sustaining and measuring a data culture, company values and everyday decision making. It also explores the nuances of how different types of data cultures work with different types of companies, what to avoid and the differences between building a data culture from scratch and changing an existing data culture from within.With this hands-on guide, senior data leader Shorful Islam takes readers through how to successfully establish or change a data culture, sharing his expertise in behavioural change psychology and two decades of experience in fostering data culture in organizations. Supported throughout by real-world examples and cases, this will be an essential read for all data leaders and anyone involved in developing a data-driven organizational culture.
Data Curious: Applying Agile Analytics for Better Business Decisions
by Carl Allchin Sarah NabelsiData has been a missing part of most academic curriculums for a long time, and we're all being affected. During challenging times, creating a data-informed culture can help you pivot quickly or prevent expensive missteps. Developing a data curious organization will take advantage of the burgeoning data resources available as a result of increasing digitalization.With this book, authors Carl Allchin and Sarah Nabelsi show today's business professionals how to become data empowered. These tech-savvy business professionals will learn data literacy fundamentals—from understanding the possibilities to asking the right questions. You'll discover how to make the right technology choices and avoid pitfalls that could put your career and company at risk.Discover what an agile, empowered, data-driven organization should look likeExamine how to use data in new ways to help your business come to lifeLearn key terms and concepts around data management and analyticsUnderstand the differences between spreadsheet analysis and a data analytics pipelineGet advice for working with data scientists and explore ways to mitigate the IT department's concerns
Data Deduplication for Data Optimization for Storage and Network Systems
by Baek-Young Choi Sejun Song Daehee KimThis book introduces fundamentals and trade-offs of data de-duplication techniques. It describes novel emerging de-duplication techniques that remove duplicate data both in storage and network in an efficient and effective manner. It explains places where duplicate data are originated, and provides solutions that remove the duplicate data. It classifies existing de-duplication techniques depending on size of unit data to be compared, the place of de-duplication, and the time of de-duplication. Chapter 3 considers redundancies in email servers and a de-duplication technique to increase reduction performance with low overhead by switching chunk-based de-duplication and file-based de-duplication. Chapter 4 develops a de-duplication technique applied for cloud-storage service where unit data to be compared are not physical-format but logical structured-format, reducing processing time efficiently. Chapter 5 displays a network de-duplication where redundant data packets sent by clients are encoded (shrunk to small-sized payload) and decoded (restored to original size payload) in routers or switches on the way to remote servers through network. Chapter 6 introduces a mobile de-duplication technique with image (JPEG) or video (MPEG) considering performance and overhead of encryption algorithm for security on mobile device.
Data Deduplication for High Performance Storage System
by Dan FengThis book comprehensively introduces data deduplication technologies for storage systems. It first presents the overview of data deduplication including its theoretical basis, basic workflow, application scenarios and its key technologies, and then the book focuses on each key technology of the deduplication to provide an insight into the evolution of the technology over the years including chunking algorithms, indexing schemes, fragmentation reduced schemes, rewriting algorithm and security solution. In particular, the state-of-the-art solutions and the newly proposed solutions are both elaborated. At the end of the book, the author discusses the fundamental trade-offs in each of deduplication design choices and propose an open-source deduplication prototype. The book with its fundamental theories and complete survey can guide the beginners, students and practitioners working on data deduplication in storage system. It also provides a compact reference in the perspective of key data deduplication technologies for those researchers in developing high performance storage solutions.
Data Democratization with Domo: Bring together every component of your business to make better data-driven decisions using Domo
by Jeff BurtenshawOvercome data challenges at record speed and cloud-scale that optimize businesses by transforming raw data into dashboards and apps which democratize data consumption, supercharging results with the cloud-based solution, DomoKey FeaturesAcquire data and automate data pipelines quickly for any data volume, variety, and velocityPresent relevant stories in dashboards and custom apps that drive favorable outcomes using DomoShare information securely and govern content including Domo content embedded in other toolsBook DescriptionDomo is a power-packed business intelligence (BI) platform that empowers organizations to track, analyze, and activate data in record time at cloud scale and performance. Data Democratization with Domo begins with an overview of the Domo ecosystem. You'll learn how to get data into the cloud with Domo data connectors and Workbench; profile datasets; use Magic ETL to transform data; work with in-memory data sculpting tools (Data Views and Beast Modes); create, edit, and link card visualizations; and create card drill paths using Domo Analyzer. Next, you'll discover options to distribute content with real-time updates using Domo Embed and digital wallboards. As you advance, you'll understand how to use alerts and webhooks to drive automated actions. You'll also build and deploy a custom app to the Domo Appstore and find out how to code Python apps, use Jupyter Notebooks, and insert R custom models. Furthermore, you'll learn how to use Auto ML to automatically evaluate dozens of models for the best fit using SageMaker and produce a predictive model as well as use Python and the Domo Command Line Interface tool to extend Domo. Finally, you'll learn how to govern and secure the entire Domo platform. By the end of this book, you'll have gained the skills you need to become a successful Domo master.What you will learnUnderstand the Domo cloud data warehouse architecture and platformAcquire data with Connectors, Workbench, and Federated QueriesSculpt data using no-code Magic ETL, Data Views, and Beast ModesProfile data with the Data Dictionary, Data Profile, and Usage toolsUse a storytelling pattern to create dashboards with Domo StoriesCreate, share, and monitor custom alerts activated using webhooksCreate custom Domo apps, use the Domo CLI, and code with the Python APIAutomate model operations with Python programming and R scriptingWho this book is forThis book is for BI developers, ETL developers, and Domo users looking for a comprehensive, end-to-end guide to exploring Domo features for BI. Chief data officers, data strategists, architects, and BI managers interested in a new paradigm for integrated cloud data storage, data transformation, storytelling, content distribution, custom app development, governance, and security will find this book useful. Business analysts seeking new ways to tell relevant stories to shape business performance will also benefit from this book. A basic understanding of Domo will be helpful.
Data Driven
by Dj Patil Hilary MasonSucceeding with data isn’t just a matter of putting Hadoop in your machine room, or hiring some physicists with crazy math skills. It requires you to develop a data culture that involves people throughout the organization. In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century.You’ll explore:Data scientist skills—and why every company needs a SpockHow the benefits of giving company-wide access to data outweigh the costsWhy data-driven organizations use the scientific method to explore and solve data problemsKey questions to help you develop a research-specific process for tackling important issuesWhat to consider when assembling your data teamDeveloping processes to keep your data team (and company) engagedChoosing technologies that are powerful, support teamwork, and easy to use and learn
Data Driven Approach Towards Disruptive Technologies: Proceedings of MIDAS 2020 (Studies in Autonomic, Data-driven and Industrial Computing)
by T. P. Singh Thinagaran Perumal Tanupriya Choudhury Ravi Tomar Hussain Falih MahdiThis book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India, during 4–5 September 2020. The book addresses the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like data-driven industrial IoT, bioinformatics, network and security, autonomous computing and various other aligned areas. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber-physical system and smart agriculture. All papers have been carefully reviewed. The book is of interest to computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.
Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)
by Sanjay Ranka Chengliang Yang Chris Delcher Elizabeth ShenkmanHealth care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics