There are many books written for the accounting and finance community. However, there are very few books written to help the non-financial career professionals who still need to understand the conceptual fundamentals of accounting and finance. In 20 years of teaching this material to non-financial professionals, Dr. Bonner has perfected a teaching approach that works to help the non-financial professional engage with the material to use financial information in leveraging their career without becoming overloaded with information that is not helpful to them. Learning this material takes repetition, application, and building the thinking processes necessary for effectiveness. Many think the challenge with finance is the math, but as this book will demonstrate, it is a conceptual problem. If you understand the conceptual framework, you will understand the math. Dr. Julie Bonner is currently a tenured professor at Central Washington University in the information technology and administrative management department. Her career has spanned business and education for over 30 years. Initially, she received a Bachelor of Science degree in accounting, whereafter she earned an MBA and then a doctorate in organizational leadership.

Provides the basic concepts of process mining techniques for pattern recognition for readers to analyze, predict, forecast, and enhance the workflow of processes Covers the entire spectrum of process mining from process discovery to operational support Discusses several process mining techniques in the context of data science and big data Contains real-life applications and case studies related to process mining theories and practices Includes detailed examples, figures, and tables for easy understanding of concepts discussed

What is the uniquely human factor in finding and using information to produce new knowledge? Is there an underlying aspect of our thinking that cannot be imitated by the AI-equipped machines that will increasingly dominate our lives? This book answers these questions, and tells us about our consciousness - its drive or intention in seeking information in the world around us, and how we are able to construct new knowledge from this information. The book is divided into three parts, each with an introduction and a conclusion that relate the theories and models presented to the real-world experience of someone using a search engine. First, Part I defines the exceptionality of human consciousness and its need for new information and how, uniquely among all other species, we frame our interactions with the world. Part II then investigates the problem of finding our real information need during information searches, and how our exceptional ability to frame our interactions with the world blocks us from finding the information we really need. Lastly, Part III details the solution to this framing problem and its operational implications for search engine design for everyone whose objective is the production of new knowledge. In this book, Charles Cole deliberately writes in a conversational style for a broader readership, keeping references to research material to the bare minimum. Replicating the structure of a detective novel, he builds his arguments towards a climax at the end of the book. For our video-game, video-on-demand times, he has visualized the ideas that form the book's thesis in over 90 original diagrams. And above all, he establishes a link between information need and knowledge production in evolutionary psychology, and thus bases his arguments in our origins as a species: how we humans naturally think, and how we naturally search for new information because our consciousness drives us to need it.

Database Semantics: Semantic Issues in Multimedia Systems reflects the state of the art of emerging research on the meaning of multimedia information, as presented during IFIP's Eighth Data Semantics Working Conference (DS-8), organized by its Working Group 2.6 on Databases, and held at Rotorua, New Zealand, in January 1999. DS-8 was planned as an active forum for researchers and practitioners focusing on those issues that involve the semantics of the information represented, stored, and manipulated by multimedia systems. Depending on the topic and state of research, issues may be covered either deeply theoretically or quite practically, or even both. These proceedings contain twenty-one papers carefully selected by an International Programme Committee and organized in six thematic areas: Video Data Modelling and Use; Image Databases; Applications of Multimedia Systems; Multimedia Modeling in General; Multimedia Information Retrieval; Semantics and Metadata. For almost every area, important topics and issues include: data modeling and query languages for media such as audio, video, and images; methodological aspects of multimedia database design; intelligent multimedia information retrieval; knowledge discovery and data mining in multimedia information; multimedia user interfaces. Three visionary keynote addresses, by famous experts Ramesh Jain, Hermann Maurer and Masao Sakauchi, set the stage for discussion and future directions for the field. The collection of papers that resulted now offers a glimpse of the excitement and enthusiasm from DS-8. Database Semantics: Semantic Issues in Multimedia Systems is suitable as a secondary text for a graduate-level course on database systems, multimedia systems, or information retrieval systems and as a reference for practitioners and researchers in industry.

Directs the attention to the smart digital healthcare system in this COVID-19 pandemic. Simulates novel investigations and how they will be beneficial in understanding the pandemic. Presents the latest ideas developed for data scientists, doctors, engineers, and economists. Analyses the various issues related to computing, AI apps, big data analytic techniques, and predictive scientific skill gaps. Explains some interesting and diverse types of challenges and data-driven healthcare applications.

The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges.

Cyberspace is changing the face of crime. For criminals it has become a place for rich collaboration and learning, not just within one country; and a place where new kinds of crimes can be carried out, and a vehicle for committing conventional crimes with unprecedented range, scale, and speed. Law enforcement faces a challenge in keeping up and dealing with this new environment. The news is not all bad - collecting and analyzing data about criminals and their activities can provide new levels of insight into what they are doing and how they are doing it. However, using data analytics requires a change of process and new skills that (so far) many law enforcement organizations have had difficulty leveraging. Cyberspace, Data Analytics, and Policing surveys the changes that cyberspace has brought to criminality and to policing with enough technical content to expose the issues and suggest ways in which law enforcement organizations can adapt. Key Features: Provides a non-technical but robust overview of how cyberspace enables new kinds of crime and changes existing crimes. Describes how criminals exploit the ability to communicate globally to learn, form groups, and acquire cybertools. Describes how law enforcement can use the ability to collect data and apply analytics to better protect society and to discover and prosecute criminals. Provides examples from open-source data of how hot spot and intelligence-led policing can benefit law enforcement. Describes how law enforcement can exploit the ability to communicate globally to collaborate in dealing with trans-national crime.

Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks. Features: Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care. Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery. Summarizes several strategies, analyses, and optimization methods for patient healthcare. Focuses on drug discovery and development by cloud computing and data-driven research The targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals.

Unique selling point: Focuses solely on entity-relationship model diagramming and design Core audience: Undergraduate CS students and professionals Place in the market: Undergraduate textbook

This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

This book is an important outcome of the Fifth World Internet Conference. It provides a comprehensive review of China's Internet development, especially the new practice and achievement in 2018. And it offers a systematic account of China's experience in Internet development and governance. This year, the book improves China's Internet Development Index System, optimizes the algorithm model, and enhances data collection, to assess and reflect Internet development more comprehensively, objectively and scientifically.

Presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research Offers a compendium of current and emerging machine learning paradigms for healthcare informatics and reflects on the diversity and complexity through the use of case studies Provides a panoramic view of data and machine learning techniques and provides an opportunity for novel insights and discovers Explores the theory and practical applications of machine learning in healthcare Includes a guided tour of machine learning algorithms, architecture design, and applications and in interdisciplinary challenges

The notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans.

This book describes systematically telemetry theory and methods for aircraft in flight test. Test targets of telemetry in flight test include airplanes, helicopters, unmanned aerial vehicles, aerostatics, carrier-based aircraft, airborne equipment (systems), weapon systems, (powered) aircraft scale models, aircraft external stores (e.g., nacelle, auxiliary tanks), and ejection seats and so on. The book collects the author's telemetry research work and presents methods that have been verified in real-world tests. The book has eight chapters: the first three discuss the theoretical basis of telemetry, while the other five focus on the methods used in flight tests.Unlike other professional textbooks, this book describes the practical telemetry theory and combines theory and engineering practice to offer a comprehensive and systematic overview of telemetry in flight test for readers.

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.

For many small businesses, organisations, clubs, artists, faith groups, voluntary organisations/charities and sole traders, applying the General Data Protection Regulation (GDPR) has been like playing a game of "Snakes and Ladders". As soon as you move along the board and climb a ladder, a snake appears, which takes you right back to where you started. Conflicting advice abounds and there is nowhere for these individuals to go for simple answers all in one place. With the threat of fines seeming around every corner, now more than ever is the time for smaller organisations to get to grips with GDPR so that they can demonstrate their compliance. GDPR: A Game of Snakes and Ladders is an easy to read reference tool, which uses simple language in bite size easily signposted chapters. Adopting a no-nonsense approach, the Regulation is explained so that organisations can comply with the minimum of fuss and deliver this compliance in the shortest timeframe without the need to resort to expensive consultants or additional staff. The book is supported by a variety of easy to follow case studies, example documents and fact sheets. The author signposts warnings and important requirements (snakes) and hints and suggestions (ladders) and also provides a section on staff training and a Game of Snakes and Ladders training slide pack. Additional resources are available on the companion website. This user-friendly book, written by a Data Protection Officer and business management specialist will help you understand the Regulation, where it applies in your organisation and how to achieve compliance (and win at the compliance game).

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

Unique selling point: Focuses solely on entity-relationship model diagramming and design Core audience: Undergraduate CS students and professionals Place in the market: Undergraduate textbook

This book describes in contributions by scientists and practitioners the development of scientific concepts, technologies, engineering techniques and tools for a service-based society. The focus is on microservices, i.e cohesive, independent processes deployed in isolation and equipped with dedicated memory persistence tools, which interact via messages. The book is structured in six parts. Part 1 "Opening" analyzes the new (and old) challenges including service design and specification, data integrity, and consistency management and provides the introductory information needed to successfully digest the remaining parts. Part 2 "Migration" discusses the issue of migration from monoliths to microservices and their loosely coupled architecture. Part 3 "Modeling" introduces a catalog and a taxonomy of the most common microservices anti-patterns and identifies common problems. It also explains the concept of RESTful conversations and presents insights from studying and developing two further modeling approaches. Next , Part 4 is dedicated to various aspects of "Development and Deployment". Part 5 then covers "Applications" of microservices, presenting case studies from Industry 4.0, Netflix, and customized SaaS examples. Eventually, Part 6 focuses on "Education" and reports on experiences made in special programs, both at academic level as a master program course and for practitioners in an industrial training. As only a joint effort between academia and industry can lead to the release of modern paradigm-based programming languages, and subsequently to the deployment of robust and scalable software systems, the book mainly targets researchers in academia and industry who develop tools and applications for microservices.

Cryptocurrency & Social Media Have Married and This is What It Looks Like Social media is a multi-billion-dollar industry where the platforms profit from user-generated content. Cryptocurrencies have arrived to end the exploitation. Cryptosocial: How Cryptocurrencies Are Changing Social Media surveys the history of social media and cryptocurrencies to show how these two unrelated technologies had a chance meeting that is changing the world. If you're one of the millions of people growing tired of legacy social media and how they take advantage of their own users, this book will open your eyes to the alternatives offering greater happiness, more freedom, and better personal and financial security. Read this book and you'll learn: What cryptosocial is all about. Which platforms and protocols you should pay attention to. Why cryptosocial media is the best alternative for Facebook, Twitter, and Snapchat. How to start using cryptosocial media. What you need to participate in decentralized social media platforms. And how you can profit from your own content, gain more control over your identity, and maintain security over your online data and personal assets.

Oracle Database Programming with Visual Basic.NET Discover a detailed treatment of the practical considerations and applications of Oracle database programming with Visual Basic 2019 Oracle Database Programming with Visual Basic.NET: Concepts, Designs, and Implementations delivers a comprehensive exploration of the foundations of Oracle database programming using Visual Basic.NET. Using Visual Basic.NET 2019, Visual Studio.NET 2019, and Oracle 18c XE, the book introduces the Oracle database development system, Oracle SQL Developer and Modeler, and teaches readers how to implement a sample database solution. The distinguished author also demonstrates the use of dotConnect for Oracle to show readers how to create an effective connection to an Oracle 18c XE database. The current versions of the .NET framework, ASP.NET, and ASP.NET 4.7 are also explored and used to offer readers the most up to date web database programming techniques available today. The book provides practical example projects and detailed, line-by-line descriptions throughout to assist readers in the development of their database programming skill. Students will also benefit from the inclusion of: A thorough introduction to databases, including definitions, examples, descriptions of keys and relationships, and some database components in popular databases, like Access, SQL, and Oracle An exploration of ADO.NET, including its architecture and components, like the DataReader class, DataSet component, DataTable component, and the command and parameter classes A discussion of Language Integrated Query (LINQ), including its architecture and components, its relationship to objects, DataSet, Oracle, and Entities An explanation of how to access data in ASP.NET and ASP.NET Web Services with multiple real project examples. Perfect for college and university students taking courses related to database programming and applications, Oracle Database Programming with Visual Basic.NET will also earn a place in the libraries of programmers and software engineers seeking a comprehensive reference for database coding in Visual Basic.NET.

Industry 4.0 is the latest technological innovation in manufacturing with the goal to increase productivity in a flexible and efficient manner. Changing the way in which manufacturers operate, this revolutionary transformation is powered by various technology advances including Big Data analytics, Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing. Big Data analytics has been identified as one of the significant components of Industry 4.0, as it provides valuable insights for smart factory management. Big Data and Industry 4.0 have the potential to reduce resource consumption and optimize processes, thereby playing a key role in achieving sustainable development. Big Data Applications in Industry 4.0 covers the recent advancements that have emerged in the field of Big Data and its applications. The book introduces the concepts and advanced tools and technologies for representing and processing Big Data. It also covers applications of Big Data in such domains as financial services, education, healthcare, biomedical research, logistics, and warehouse management. Researchers, students, scientists, engineers, and statisticians can turn to this book to learn about concepts, technologies, and applications that solve real-world problems. Features An introduction to data science and the types of data analytics methods accessible today An overview of data integration concepts, methodologies, and solutions A general framework of forecasting principles and applications, as well as basic forecasting models including naive, moving average, and exponential smoothing models A detailed roadmap of the Big Data evolution and its related technological transformation in computing, along with a brief description of related terminologies The application of Industry 4.0 and Big Data in the field of education The features, prospects, and significant role of Big Data in the banking industry, as well as various use cases of Big Data in banking, finance services, and insurance Implementing a Data Lake (DL) in the cloud and the significance of a data lake in decision making

- Curating Social Data - Summarizing Social Data - Analyzing Social Data - Social Data Analytics Applications: Trust, Recommender Systems, Cognitive Analytics

This book unfolds ways to transform data into innovative solutions perceived as new remarkable and meaningful value. It offers practical views of the concepts and techniques readers need to get the most out of their large-scale research and data mining projects. It strides them through the data-analytical thinking, circumvents the difficulty in deciphering complex data systems and obtaining commercialization value from the data. Also known as data-driven science, soft computing and data mining disciplines cover a broad spectrum, an interdisciplinary field of scientific methods and processes. The book, Recent Advances in Soft Computing and Data Mining, delivers sufficient knowledge to tackle a wide range of issues seen in complex systems. This is done by exploring a vast combination of practices and applications by incorporating these two domains. To thrive in these data-driven ecosystems, researchers, data analysts, and practitioners must choose the best design to approach the problem with the most efficient tools and techniques. To thrive in these data-driven ecosystems, researchers, data analysts, and practitioners must understand the design choice and options of these approaches, thus to better appreciate the concepts, tools, and techniques used.

This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Personalized Psychiatry - Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health.