Learning analytics is one of the most important research issues in the field of educational technology. By analyzing logs and records in educational databases and systems, it can provide useful information to teachers, learners, and decision makers - information which they can use to improve teaching strategies, learning performances, and educational policies. However, it is a great challenge for most researchers to efficiently analyze educational data in a meaningful way. This book presents various learning analytics approaches and applications, including the process of determining the coding scheme, analyzing the collected data, and interpreting the findings. This book was originally published as a special issue of Interactive Learning Environments.

Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the 'What', 'How', and 'Why' of Big Data. Features Identifies the primary drivers of Big Data Walks readers through the theory, methods and technology of Big Data Explains how to handle the 4 V's of Big Data in order to extract value for better business decision making Shows how and why data connectors are critical and necessary for Agile text analytics Includes in-depth tutorials to perform necessary set-ups, installation, configuration and execution of important tasks Explains the command line as well as GUI interface to a powerful data exchange tool between Hadoop and legacy r-dbms databases

This book is about the rise of data as a driver of innovation and economic growth. It charts the evolution of business data as a valuable resource and explores some of the key business, economic and social issues surrounding the data-driven revolution we are currently going through. Readers will gain an understanding of the historical underpinnings of the data business and why the collection and use of data has been driven by commercial needs. Readers will also gain insights into the rise of the modern data-driven technology giants, their business models and the reasons for their success. Alongside this, some of the key social issues including privacy are considered and the challenges these pose to policymakers and regulators. Finally, the impact of pervasive computing and the Internet of Things (IoT) is explored in the context of the new sources of data that are being generated. This book is useful for students and practitioners wanting to better understand the origins and drivers of the current technological revolution and the key role that data plays in innovation and business success.

Fully updated and expanded from the previous edition, A Practical Guide to Database Design, Second Edition is intended for those involved in the design or development of a database system or application. It begins by illustrating how to develop a Third Normal Form data model where data is placed "where it belongs". The reader is taken step-by-step through the Normalization process, first using a simple then a more complex set of data requirements. Next, usage analysis for each Logical Data Model is reviewed and a Physical Data Model is produced that will satisfy user performance requirements. Finally, each Physical Data Model is used as input to create databases using both Microsoft Access and SQL Server. The book next shows how to use an industry-leading data modeling tool to define and manage logical and physical data models, and how to create Data Definition Language statements to create or update a database running in SQL Server, Oracle, or other type of DBMS. One chapter is devoted to illustrating how Microsoft Access can be used to create user interfaces to review and update underlying tables in that database as well as tables residing in SQL Server or Oracle. For users involved with Cyber activity or support, one chapter illustrates how to extract records of interest from a log file using PERL, then shows how to load these extracted records into one or more SQL Server "tracking" tables adding status flags for analysts to use when reviewing activity of interest. These status flags are used to flag/mark collected records as "Reviewed", "Pending" (currently being analyzed) and "Resolved". The last chapter then shows how to build a web-based GUI using PHP to query these tracking tables and allow an analyst to review new activity, flag items that need to be investigated, and finally flag items that have been investigated and resolved. Note that the book has complete code/scripts for both PERL and the PHP GUI.

From the Foreword: "[This book] provides a comprehensive overview of the fundamental concepts in healthcare process management as well as some advanced topics in the cutting-edge research of the closely related areas. This book is ideal for graduate students and practitioners who want to build the foundations and develop novel contributions in healthcare process modeling and management." --Christopher Yang, Drexel University Process modeling and process management are traversal disciplines which have earned more and more relevance over the last two decades. Several research areas are involved within these disciplines, including database systems, database management, information systems, ERP, operations research, formal languages, and logic. Process Modeling and Management for Healthcare provides the reader with an in-depth analysis of what process modeling and process management techniques can do in healthcare, the major challenges faced, and those challenges remaining to be faced. The book features contributions from leading authors in the field. The book is structured into two parts. Part one covers fundamentals and basic concepts in healthcare. It explores the architecture of a process management environment, the flexibility of a process model, and the compliance of a process model. It also features a real application domain of patients suffering from age-related macular degeneration. Part two of the book includes advanced topics from the leading frontiers of scientific research on process management and healthcare. This section of the book covers software metrics to measure features of the process model as a software artifact. It includes process analysis to discover the formal properties of the process model prior to deploying it in real application domains. Abnormal situations and exceptions, as well as temporal clinical guidelines, are also presented in depth Pro.

Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.

Optimize Your Chemical Database Design and Use of Relational Databases in Chemistry helps programmers and users improve their ability to search and manipulate chemical structures and information, especially when using chemical database "cartridges". It illustrates how the organizational, data integrity, and extensibility properties of relational databases are best utilized when working with chemical information. The author facilitates an understanding of existing relational database schemas and shows how to design new schemas that contain tables of data and chemical structures. By using database extension cartridges, he provides methods to properly store and search chemical structures. He explains how to download and install a fully functioning database using free, open-source chemical extension cartridges within PostgreSQL. The author also discusses how to access a database on a computer network using both new and existing applications. Through examples of good database design, this book shows you that relational databases are the best way to store, search, and operate on chemical information.

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases

Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text. Features Gets you quickly using R as a problem-solving tool Uses RStudio's integrated development environment Shows how to interface R with SQLite Includes examples using R's Rattle graphical user interface Requires no prior knowledge of R, machine learning, or computer programming Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts Includes datasets from several areas, including business, health and medicine, and science About the Author Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.

- the book can be used by beginners in the field, tracking from basic principles to how to bend the rules, in reader-friendly language throughout - the book is based on a popular blog which dovetails as a fantastic companion website: https://questionsindataviz.com/ - the author is a very experienced and well-respected practitioner in the field, with a good-size following on social media: https://twitter.com/theneilrichards

- the book can be used by beginners in the field, tracking from basic principles to how to bend the rules, in reader-friendly language throughout - the book is based on a popular blog which dovetails as a fantastic companion website: https://questionsindataviz.com/ - the author is a very experienced and well-respected practitioner in the field, with a good-size following on social media: https://twitter.com/theneilrichards

This book introduces computational advertising, and Internet monetization. It provides a macroscopic understanding of how consumer products in the Internet era push user experience and monetization to the limit. Part One of the book focuses on the basic problems and background knowledge of online advertising. Part Two targets the product, operations, and sales staff, as well as high-level decision makers of the Internet products. It explains the market structure, trading models, and the main products in computational advertising. Part Three targets systems, algorithms, and architects, and focuses on the key technical challenges of different advertising products. Features * Introduces computational advertising and Internet monetization * Covers data processing, utilization, and trading * Uses business logic as the driving force to explain online advertising products and technology advancement * Explores the products and the technologies of computational advertising, to provide insights on the realization of personalization systems, constrained optimization, data monetization and trading, and other practical industry problems * Includes case studies and code snippets

Urban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an ideal onramp for the study of urban informatics and how to translate novel data sets into new insights and practical tools. The book follows a unique pedagogical approach developed by the author to enable students to build skills by pursuing projects that inspire and motivate them. Each chapter has an Exploratory Data Assignment that prompts readers to practice their new skills on a data set of their choice. These assignments guide readers through the process of becoming familiar with the contents of a novel data set and communicating meaningful insights from the data to others. Key Features: The technical curriculum consists of both data management and analytics, including both as needed to become acquainted with and reveal the content of a new data set. Content that is contextualized in real-world applications relevant to community concerns. Unit-level assignments that educators might use as midterms or otherwise. These include Community Experience assignments that prompt students to evaluate the assumptions they have made about their data against real world information. All data sets are publicly available through the Boston Data Portal.

This volume provides informative chapters on the emerging issues, challenges, and new methods and state-of-the-art technologies on the Internet of Things and blockchain technology. It presents case studies and solutions that can be applied in the current business scenario, resolving challenges and providing solutions by integrating IoT with blockchain technology. The chapters discuss how the Internet of Things (IoT) represents a revolution of the Internet that can connect nearly all environment devices over the Internet to share data to create novel services and applications for improving quality of life. Although the centralized IoT system provides countless benefits, it raises several challenges. The volume presents IoT techniques and methodologies, blockchain techniques and methodologies, and case studies and applications for data mining algorithms, heart rate monitoring, climate prediction, disease prediction, security issues, automotive supply chains, voting prediction, forecasting particulate matter pollution, customer relationship management, and more.

The internet has launched the world into an era into which enormous amounts of data are generated every day through technologies with both positive and negative consequences. This often refers to big data . This book explores big data in organisations operating in the criminology and criminal justice fields. Big data entails a major disruption in the ways we think about and do things, which certainly applies to most organisations including those operating in the criminology and criminal justice fields. Big data is currently disrupting processes in most organisations - how different organisations collaborate with one another, how organisations develop products or services, how organisations can identify, recruit, and evaluate talent, how organisations can make better decisions based on empirical evidence rather than intuition, and how organisations can quickly implement any transformation plan, to name a few. All these processes are important to tap into, but two underlying processes are critical to establish a foundation that will permit organisations to flourish and thrive in the era of big data - creating a culture more receptive to big data and implementing a systematic data analytics-driven process within the organisation. Written in a clear and direct style, this book will appeal to students and scholars in criminology, criminal justice, sociology, and cultural studies but also to government agencies, corporate and non-corporate organisations, or virtually any other institution impacted by big data.

Large-Scale 3D Data Integration: Challenges and Opportunities examines the fundamental aspects of 3D geo-information, focusing on the latest developments in 3D GIS (geographic information) and AEC (architecture, engineering, construction) systems. This book addresses policy makers, designers and engineers, and individuals that need to overcome obstacles in integrating modeling perspectives and data. Organized into four major parts, the book begins by presenting a historical overview of the issues involved in integrating GIS and AEC. Part II then focuses on the data issue from several viewpoints: data collection; database structures and representation; database management; and visualization. Part III covers the areas of semantics, ontology, and standardization from a theoretical perspective and details many of the best examples of this approach in developing real-world applications. The book concludes with contributions that focus on recent advances in virtual geographic environments and alternative modeling schemes for the potential AEC/GIS interface.

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues. This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence. Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.

Whether you are a project manager looking to lead blockchain projects, a developer who would like to create blockchain-based applications, or a student with an interest, this book will provide you with the foundational understanding that you need. You have probably noticed that blockchains are growing in popularity. Governments are investigating Digital Currencies, supply chains are adopting Digital Ledgers, games makers and artists are developing NFTs (Non-Fungible Tokens), and new use-cases are emerging regularly. With such growth, many people will find themselves needing to understand how these technologies work. There will be new project teams, with technical leads managing blockchain projects and developers creating distributed applications. This book is great for them as it explains the concepts on which blockchain technologies are based, in simple terms. We will discuss and explain topics such as hashing, Merkle trees, nodes, mining, proof of work and proof of stake, consensus mechanisms encryption, vulnerabilities, and much more. The structures and principles described will be relevant for developers and managers alike, and will be demonstrated through relevant examples throughout the text. If you are looking to understand this exciting new technology, this is the book for you.

Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors' own research work, the book takes a practical approach to the subject. The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification. Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Oracle 11i E-Business Suite from the Front Lines is the first book to compile the tips, techniques, and practical advice for administering Oracle E-Business Suite 11i. The author examines Active Directory Utilities, patching, cloning, and the new features that 11i brings to the market. The book benefits those with limited experience with Oracle Application but with more extensive background in Oracle Database Administration. This volume is valuable to systems administrators or DBAs who have experience with older versions of Oracle Financials and want to expand their knowledge to include the changes inherent in 11i. The book details the steps in installing a new 11i environment, and explains the process of upgrading from a 10.7 or an 11.0.3 release. It also explores the techniques and results of migrating from one maintenance release of 11i to another. This analysis offers you real-world hints and recommendations to help you with day-to-day tuning, troubleshooting, and maintenance and will help you deliver reliable service to your end users. It is also a helpful tool that enables managers and co-workers to understand the daily challenges that Apps DBAs face.

Big Data is everywhere. It shapes our lives in more ways than we know and understand. This comprehensive introduction unravels the complex terabytes that will continue to shape our lives in ways imagined and unimagined. Drawing on case studies like Amazon, Facebook, the FIFA World Cup and the Aadhaar scheme, this book looks at how Big Data is changing the way we behave, consume and respond to situations in the digital age. It looks at how Big Data has the potential to transform disaster management and healthcare, as well as prove to be authoritarian and exploitative in the wrong hands. The latest offering from the authors of Artificial Intelligence: Evolution, Ethics and Public Policy, this accessibly written volume is essential for the researcher in science and technology studies, media and culture studies, public policy and digital humanities, as well as being a beacon for the general reader to make sense of the digital age.

Most widely available approaches to semantic integration provide ad-hoc, non-systematic, subjective manual mappings that lead to procrustean amalgamations to fit the target standard, an outcome that pleases no one. Written by experts in the field, Theories of Geographic Concepts: Ontological Approaches to Semantic Integration emphasizes the real issues involved in integrating existing geo-ontologies. The book addresses theoretical, formal, and pragmatic issues of geographic knowledge representation and integration based on an ontological approach. The authors highlight the importance of philosophical, cognitive, and formal theories in preserving the semantics of geographic concepts during ontology development and integration. They elucidate major theoretical issues, then introduce a number of formal tools. The book delineates a general framework with the necessary processes and guidelines to ontology integration and applies it to a selection of ontology integration cases. It concludes with a retrospection of key issues and identifies open research questions. Copiously illustrated, the book contains more than 80 illustrations and several examples to various approaches that provide a better understanding of the complexity of ontology integration tasks. The authors provide guidance on selecting the most appropriate approach and details on its application to indicative integration problems.

Expert Bytes: Computer Expertise in Forensic Documents - Players, Needs, Resources and Pitfalls -introduces computer scientists and forensic document examiners to the computer expertise of forensic documents and assists them with the design of research projects in this interdisciplinary field. This is not a textbook on how to perform the actual forensic document expertise or program expertise software, but a project design guide, an anthropological inquiry, and a technology, market, and policies review. After reading this book you will have deepened your knowledge on: What computational expertise of forensic documents is What has been done in the field so far and what the future looks like What the expertise is worth, what its public image is, and how to improve both Who is doing what in the field, where, and for how much How the expertise software functions The primary target readers are computer scientists and forensic document examiners, at the student and professional level. Paleographers, historians of science and technology, and scientific policy makers can also profit from the book. Concise and practical, featuring an attractive and functional layout design, the book is supplemented with graphical data representations, statistics, resource lists, and extensive references to facilitate further study.