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This book presents techniques for querying and managing Linked Data that is available on today's Web. It shows how the abundance of Linked Data can serve as fertile ground for research and commercial applications. While the book covers query processing extensively, the Linked Data abstraction furnishes more than a mechanism for collecting, integrating, and querying data from the open Web-the Linked Data technology stack also allows for controlled, sophisticated applications deployed in an enterprise environment.

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.

This book introduces the semantic aspects of natural language processing and its applications. Topics covered include: measuring word meaning similarity, multi-lingual querying, and parametric theory, named entity recognition, semantics, query language, and the nature of language. The book also emphasizes the portions of mathematics needed to under

Machine Learning for Healthcare: Handling and Managing Data will provide in-depth information about handling and managing healthcare data through machine learning methods. This book will express the long-standing challenges in healthcare informatics and provide rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. This book may appeal to Computer Science/Information Technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: * A complete overview of big data and its notable characteristics * Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases * Comprehensive coverage of data mining, text analytics, and machine learning algorithms * A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

A hands on guide to web scraping and text mining for both beginners and experienced users of R * Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL. * Provides basic techniques to query web documents and data sets (XPath and regular expressions). * An extensive set of exercises are presented to guide the reader through each technique. * Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. * Case studies are featured throughout along with examples for each technique presented. * R code and solutions to exercises featured in the book are provided on a supporting website.

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: * Discovering activity patterns that emerge from behavior-based sensor data * Recognizing occurrences of predefined or discovered activities in real time * Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: • Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. • Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. • Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science. Preface Editors Contributors Part I Data Science: Theory, Concepts, and Algorithms Framework for Visualization of GeoSpatial Query Processing by Integrating MongoDB with Spark S.Vasavi, P.Vamsi Krishna, Anu A. Gokhale A Study on Meta-heuristic based Neural Networks for Image Segmentation Purposes Navid Razmjooy, Vania V. Estrela, Hermes J. Loschi Study and Analysis of a Feature Subset Selection Technique using Penguin Search Optimization Algorithm Agnip Dasgupta, Ardhendu Banerjee, Aniket Ghosh Dastidar, Antara Barman, Sanjay Chakraborty A Physical Design Strategy on a NoSQL DBMS Marcos Jota, Marlene Goncalves, Ritces Parra Large-Scale Distributed Stream Data Collection Schemes Tomoya Kawakami, Tomoki Yoshihisa, and Yuuichi Teranishic Part II Data Design and Analysis Big Data Analysis and Management in Healthcare R.DHAYA, R.KANTHAVEL, FAHAD ALGARNI, M.DEVI Healthcare Analytics: A Case Study Approach using the Framingham Heart Study Carol Hargreaves Bioinformatics Analysis of Dysfunctional (mutated) Proteins of Cardiac Ion Channels Underlying the Brugada Syndrome Carlos Polanco, Manlio F. Márquez, Vladimir N. Uversky, Thomas Buhse, and Miguel Arias Estrada Discrimination of Healthy Skin, Superficial Epidermal Burns and Full-thickness Burns from 2D-Coloured Images Using Machine Learning Aliyu Abubakar, Hassan Ugail, Ali Maina Bukar, Kirsty M. Smith A Study and Analysis of an Emotion Classification and State Transition System in Brain Computer Interfacing Subhadip Pal, Shailesh Shaw, Tarun Saurabh, Yashwant Kumar, Sanjay Chakraborty Part III Applications and New Trends in Data Science Comparison of Gradient and Textural Features for Writer Retrieval in Handwritten Documents Mohamed Lamine Bouibed, Hassiba Nemmour and Youcef Chibani A Supervised Guest Satisfaction Classification with Reviews Text and Ratings Himanshu Sharma, Aakash and Anu G. Aggarwal Sentiment Analysis for Decision Making Using Machine Learning Algorithms Mohamed Alloghani, Thar Baker, Abir Hussain, Mohammed Khalaf, Jamila MUSTAFINA, Mohammed Al-Khafajiy Deep Learning Model: Emotion Recognition from Continuous Action Video R. Santhosh kumar, M. Kalaiselvi Geetha Index Qurban A Memon has contributed at levels of teaching, research, and community service in the area of electrical and computer engineering. He has authored/co‐authored ninety seven publications over a period of about eighteen years of his academic career. Shakeel Khoja is a Professor at Faculty of Computer Science, IBA and a Commonwealth Academic Fellow. He carries an overall professional experience of over 18 years and has over fifty research publications to his credit. He is also a reviewer of number of journals and conferences in the field of computer science.

This volume is the first focused effort to compile state-of-the-art research and address future directions of UGC. It explains how to collect, index, and analyze UGC to uncover social trends and user habits. The book describes how to mine various media, including social annotation, music information retrieval, and networks, and discusses the mining and searching of different types of UGC, such as Wikis and blogs. It also presents many applications of UGC, including the use of UGC to answer questions and summarize information.

Research 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.