Text as Data
A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text-representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides-computer science and social science, the qualitative and the quantitative, and industry and academia-Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain.Overview of how to use text as dataResearch design for a world of data delugeExamples from across the social sciences and industry
Hands-On Azure Data Platform
Plan, build, deploy, and monitor data solutions on AzureKey FeaturesWork with PostgreSQL, MySQL, and CosmosDB databases on Microsoft Azure.Work with whole data architecture, leverage Azure Storage, Azure Synapse, and Azure Data Lake.Data integration strategies with Azure Data Factory and Data Bricks.Description'Hands-On Azure Data Platform' helps readers get a fundamental understanding of the Database, Data Warehouse, and Data Lake and their management on the Azure Data Platform.The book describes how to work efficiently with Relational and Non-Relational Databases, Azure Synapse Analytics, and Azure Data Lake. The readers will use Azure Databricks and Azure Data Factory to experience data processing and transformation. The book delves deeply into topics like continuous integration, continuous delivery, and the use of Azure DevOps. The book focuses on the integration of Azure DevOps with CI/CD pipelines for data ops solutions. The book teaches readers how to migrate data from an on-premises system or another cloud service provider to Azure.After reading the book, readers will develop end-to-end data solutions using the Azure data platform. Additionally, data engineers and ETL developers can streamline their ETL operations using various efficient Azure services.What you will learnIn-depth knowledge of the principles of the data warehouse and the data lake.Acquaint yourself with Azure Storage Files, Blobs, and Queues.Create relational databases on the Azure platform using SQL, PostgreSQL, and MySQL.With Cosmos DB, you can create extremely scalable databases and data warehouses.Utilize Azure Databricks and Data Factory to develop data integration solutions.Who this book is forThis book is designed for big data engineers, data architects, and cloud engineers who want to understand how to use the Azure Data Platform to build enterprise-grade solutions. Learning about databases and the Azure Data Platform would be helpful but not necessary.Table of Contents1. Getting Started with the Azure Data Platform2. Working with Relational Databases on Azure3. Working with Azure Synapse Analytics4. Working with Azure Data Lake5. Working with Azure Cosmos DB6. Working with Azure Databricks7. Working with Azure Data Factory8. DevOps with the Azure Data Platform9. Planning and Migrating On-Premises Azure Workloads to the Azure Data platform10. Design and Implement Data Solutions on AzureRead more
Illumination of Artificial Intelligence in Cybersecurity and Forensics
A Practical Experience Applying Security Audit Techniques in an Industrial Healthcare System.- Feature Extraction and Artificial Intelligence-Based Intrusion Detection Model for a Secure Internet of Things Networks.- Intrusion Detection using Anomaly Detection Algorithm and Snort.- Research Perspective on Digital Forensic Tools and Investigation Process.- Intelligent Authentication Framework for Internet of Medical Things (IoMT).- Parallel Faces Recognition Attendance System with Anti-spoofing using Convolutional Neural Network.- A Systematic Literature Review on Face Morphing Attack Detection (MAD).- Averaging Dimensionality Reduction and Feature Level Fusion for Post-Processed Morphed Face Image Attack Detection.- A Systematic Literature Review on Forensics in Cloud, IoT, AI & Blockchain.- Predictive Forensic Based - Characterization of Hidden Elements in Criminal Networks using Baum-Welch Optimization Technique.- An Integrated IDS using ICA-based Feature Selection and SVM Classification Method.- A Binary Firefly Algorithm Based Feature Selection Method on High Dimensional Intrusion Detection Data.- Graphical Based Authentication Method Combined with City Block Distance for Electronic Payment System.- Authenticated Encryption to Prevent Cyberattacks in images.- Machine Learning in Automated Detection of Ransomware: Scope, Benefits and Challenges.
Advanced Computing
This volume constitutes reviewed and selected papers from the 11th International Advanced Computing Conference, IACC 2021, held in December 2021.The 47 full papers and 4 short papers presented in the volume were thorougly reviewed and selected from 246 submissions. The papers are organized in the following topical sections: application of artificial intelligence and machine learning in healthcare; application of AI for emotion and behaviour prediction; problem solving using reinforcement learning and analysis of data; advance uses of RNN and regression techniques; special intervention of AI.
Data Warehousing and Analytics
This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.
Machine Learning, Optimization, and Data Science
This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021.The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
Reasoning Web. Declarative Artificial Intelligence
The purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers.The broad theme of this year's summer school was again "Declarative Artificial Intelligence" and it covered various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications. The following eight lectures were presented during the school: Foundations of Graph Path Query Languages; On Combining Ontologies and Rules; Modelling Symbolic Knowledge Using Neural Representations; Mining the Semantic Web with Machine Learning: Main Issues That Need to Be Known; Temporal ASP: From Logical Foundations to Practical Use with telingo; A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs; and Score-Based Explanations in Data Management and Machine Learning.
Advances in the Convergence of Blockchain and Artificial Intelligence
Blockchain (BC) and artificial intelligence (AI) are currently two of the hottest computer science topics and their future seems bright. However, their convergence is not straightforward, and more research is needed in both fields. Thus, this book presents some of the latest advances in the convergence of BC and AI, gives useful guidelines for future researchers on how BC can help AI and how AI can become smarter, thanks to the use of BC. This book specifically analyzes the past of BC through the history of Bitcoin and then looks into the future: from massive internet-of-things (IoT) deployments, to the so-called metaverse, and to the next generation of AI-powered BC-based cyber secured applications.
Emerging Technologies for Education
This book constitutes the refereed conference proceedings of the 6th International Symposium on Emerging Technologies for Education, SETE 2021, held in Zhuhai, China in November 2021. 35 full papers were accepted together with 8 short papers out of 58 submissions. The papers focus on the following subjects: Emerging Technologies for Education, Digital Technology, Creativity, and Education; Education Technology (Edtech) and ICT for Education; Education + AI; Adaptive Learning, Emotion and Behaviour Recognition and Understanding in Education; as well as papers from the International Symposium on User Modeling and Language Learning (UMLL2021) and the International Workshop on Educational Technology for Language Learning (ETLL 2021).
Time Series Analysis on AWS
Leverage AWS AI/ML managed services to generate value from your time series dataKey Features: Solve modern time series analysis problems such as forecasting and anomaly detectionGain a solid understanding of AWS AI/ML managed services and apply them to your business problemsExplore different algorithms to build applications that leverage time series dataBook Description: Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes.The book begins with Amazon Forecast, where you'll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You'll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you'll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data.By the end of this AWS book, you'll have understood how to use the three AWS AI services effectively to perform time series analysis.What You Will Learn: Understand how time series data differs from other types of dataExplore the key challenges that can be solved using time series dataForecast future values of business metrics using Amazon ForecastDetect anomalies and deliver forewarnings using Lookout for EquipmentDetect anomalies in business metrics using Amazon Lookout for MetricsVisualize your predictions to reduce the time to extract insightsWho this book is for: If you're a data analyst, business analyst, or data scientist looking to analyze time series data effectively for solving business problems, this is the book for you. Basic statistics knowledge is assumed, but no machine learning knowledge is necessary. Prior experience with time series data and how it relates to various business problems will help you get the most out of this book. This guide will also help machine learning practitioners find new ways to leverage their skills to build effective time series-based applications.
Chatbot Research and Design
This book constitutes the proceedings of the 5th International Workshop on Chatbot Research and Design, CONVERSATIONS 2021, which was held during November 2021.Due to COVID-19 pandemic the conference was held online.The 12 papers included in this volume were carefully reviewed and selected from a total of 25 submissions. The papers in the proceedings are structured in four topical groups: Chatbot User Insight, Chatbots Supporting Collaboration and Social Interaction, and Chatbot UX and Design.
Expert SQL Server 2005 Development
This book offers a unique perspective on database development issues. It first presents and explains advanced techniques then demonstrates their use in various business scenarios. The goal is expert mastery of both concepts and their application.
Mathletics
How to use math to improve performance and predict outcomes in professional sports Mathletics reveals the mathematical methods top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical skills they need to enhance their understanding and enjoyment of their favorite sports--and maybe even gain the outside edge to winning bets. This second edition features new data, new players and teams, and new chapters on soccer, e-sports, golf, volleyball, gambling Calcuttas, analysis of camera data, Bayesian inference, ridge regression, and other statistical techniques. After reading Mathletics, you will understand why baseball teams should almost never bunt; why football overtime systems are unfair; why points, rebounds, and assists aren't enough to determine who's the NBA's best player; and more.
Practitioner’s Guide to Data Science
Covers Data Science concepts, processes, and the real-world hands-on use cases.Key FeaturesCovers the journey from a basic programmer to an effective Data Science developer.Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP.Implementation of MLOps using Microsoft Azure DevOps.Description"How is the Data Science project to be implemented?" has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do.This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects.The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it.By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models.What you will learnOrganize Data Science projects using CRISP-DM and Microsoft TDSP.Learn to acquire and explore data using Python visualizations.Get well versed with the implementation of data pre-processing and Feature Engineering.Understand algorithm selection, model development, and model evaluation.Hands-on with Azure ML Service, its architecture, and capabilities.Learn to use Azure ML SDK and MLOps for implementing real-world use cases.Who this book is forThis book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions.Table of Contents1. Data Science for Business2. Data Science Project Methodologies and Team Processes3. Business Understanding and Its Data Landscape4. Acquire, Explore, and Analyze Data5. Pre-processing and Preparing Data6. Developing a Machine Learning Model7. Lap Around Azure ML Service8. Deploying and Managing ModelsRead more
The Road to Azure Cost Governance
Get to grips with Microsoft Azure cost management and gain complete, reliable, and sustainable control of your cloud spendKey Features: Explore resource rightsizing and cleanup methods and their implementationLearn key resource usage optimization conceptsUnderstand app optimization and plan for optimized and sustainable cloud native applicationsBook Description: Cloud teams and ICT cost controllers working with Azure will be able to put their knowledge to work with this practical guide, introducing a process model for structured cost governance. The Road to Azure Cost Governance is a must-read if you find yourself facing the harsh reality of monthly cloud costs gradually getting out of control.Starting with how resources are created and managed, everything you need to know in order to track, display, optimize, rightsize, and clean up cloud resources will be tackled with a workflow approach that will leave the choice of operation to you (be it the Azure CLI, automation, logic apps, or even custom code). Using real-world datasets, you'll learn everything from basic cost management to modeling your cloud spend across your technical resources in a sustainable way. The book will also show you how to create a recursive optimization process that will give you full control of spending and savings, while helping you reserve budget for future cloud projects and innovation.By the end of this Azure book, you'll have a clear understanding and control of your cloud spend along with knowledge of a number of cost-saving techniques used by companies around the world, application optimization patterns, and the carbon impact of your cloud infrastructure.What You Will Learn: Use Azure reporting, monitoring, and configurations to model your cloud resources and make costs clearerDiscover resource-saving techniques and put them into practiceEstablish a continuous clean-up and rightsizing processExplore and implement automation to drive recurrent savingsFind out how to use Azure Reservations in the best possible wayGet started with building cloud native, cost-optimized applicationsGet to grips with implementing cost- and carbon-aware applications on AzureWho this book is for: If you're someone who deals with Azure cloud costs and has a technical background, this book will help you understand and control your cloud spending. This book is for decision-makers, cloud managers, cloud architects, cost controllers, and software solution professionals working with Microsoft cloud services in Azure and looking to build optimized solutions for their enterprise operations.
Meeting the Challenges of Data Quality Management
Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.
Microsoft Power Bi for Dummies
Reveal the insights behind your company's data with Microsoft Power BI Microsoft Power BI allows intuitive access to data that can power intelligent business decisions and insightful strategies. The question is, do you have the Power BI skills to make your organization's numbers spill their secrets? In Microsoft Power BI For Dummies, expert lecturer, consultant, and author Jack Hyman delivers a start-to-finish guide to applying the Power BI platform to your own firm's data. You'll discover how to start exploring your data sources, build data models, visualize your results, and create compelling reports that motivate decisive action. Tackle the basics of Microsoft Power BI and, when you're done with that, move on to advanced functions like accessing data with DAX and app integrations Guide your organization's direction and decisions with rock-solid conclusions based on real-world data Impress your bosses and confidently lead your direct reports with exciting insights drawn from Power BI's useful visualization tools It's one thing for your company to have data at its disposal. It's another thing entirely to know what to do with it. Microsoft Power BI For Dummies is the straightforward blueprint you need to apply one of the most powerful business intelligence tools on the market to your firm's existing data.
Decision Intelligence for Dummies
Learn to use, and not be used by, data to make more insightful decisions The availability of data and various forms of AI unlock countless possibilities for business decision makers. But what do you do when you feel pressured to cede your position in the decision-making process altogether? Decision Intelligence For Dummies pumps the brakes on the growing trend to take human beings out of the decision loop and walks you through the best way to make data-informed but human-driven decisions. The book shows you how to achieve maximum flexibility by using every available resource, and not just raw data, to make the most insightful decisions possible. In this timely book, you'll learn to: Make data a means to an end, rather than an end in itself, by expanding your decision-making inquiries Find a new path to solid decisions that includes, but isn't dominated, by quantitative data Measure the results of your new framework to prove its effectiveness and efficiency and expand it to a whole team or company Perfect for business leaders in technology and finance, Decision Intelligence For Dummies is ideal for anyone who recognizes that data is not the only powerful tool in your decision-making toolbox. This book shows you how to be guided, and not ruled, by the data.
Classification Made Relevant
Classification Made Relevant: How Scientists Build and Use Classifications and Ontologies explains how classifications and ontologies are designed and used to analyze scientific information. The book presents the fundamentals of classification, leading up to a description of how computer scientists use object-oriented programming languages to model classifications and ontologies. Numerous examples are chosen from the Classification of Life, the Periodic Table of the Elements, and the symmetry relationships contained within the Classification Theorem of Finite Simple Groups. When these three classifications are tied together, they provide a relational hierarchy connecting all of the natural sciences. The book's chapters introduce and describe general concepts that can be understood by any intelligent reader. With each new concept, they follow practical examples selected from various scientific disciplines. In these cases, technical points and specialized vocabulary are linked to glossary items where the item is clarified and expanded.
A Primer on Business Analytics
This book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team - from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.
Big Data Analytics
This book constitutes the proceedings of the 8th International Conference on Big Data Analytics, BDA 2021, which took place during December 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 full and 3 short papers included in this volume were carefully reviewed and selected from 41 submissions. The contributions were organized in topical sections named as follows: medical and health applications; machine/deep learning; IoTs, sensors, and networks; fundamentation; pattern mining and data analytics.
A Primer on Business Analytics
This book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team - from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.
Personalized Machine Learning
Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
Advanced Hybrid Information Processing
This two-volume set constitutes the post-conference proceedings of the 5th EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2021, held in October 2021. Due to COVID-19 the conference was held virtually. The 94 papers presented were selected from 254 submissions and focus on theory and application of hybrid information processing technology for smarter and more effective research and application. The theme of ADHIP 2020 was "Social hybrid data processing". The papers are named in topical sections as follows: Intelligent algorithms in complex environment; AI system research and model design; Method research on Internet of Things technology; Research and analysis with intelligent education.
Advanced Hybrid Information Processing
This two-volume set constitutes the post-conference proceedings of the 5th EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2021, held in October 2021. Due to COVID-19 the conference was held virtually. The 94 papers presented were selected from 254 submissions and focus on theory and application of hybrid information processing technology for smarter and more effective research and application. The theme of ADHIP 2020 was "Social hybrid data processing". The papers are named in topical sections as follows: Intelligent algorithms in complex environment; AI system research and model design; Method research on Internet of Things technology; Research and analysis with intelligent education.
Algorithms and Discrete Applied Mathematics
This book constitutes the proceedings of the 8th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2022, which was held in Puducherry, India, during February 10-12, 2022. The 24 papers presented in this volume were carefully reviewed and selected from 80 submissions. The papers were organized in topical sections named: graph theory, graph algorithms, computational geometry, algorithms and optimization.
Algebra and Geometry with Python
This book teaches algebra and geometry. The authors dedicate chapters to the key issues of matrices, linear equations, matrix algorithms, vector spaces, lines, planes, second-order curves, and elliptic curves. The text is supported throughout with problems, and the authors have included source code in Python in the book. The book is suitable for advanced undergraduate and graduate students in computer science.
Practical Aspects of Declarative Languages
This book constitutes the refereed proceedings of the 24th International Conference on Practical Aspects of Declarative Languages, PADL 2022, held in Philadelphia, PA, USA, during January 17-18, 2022. The 9 full papers and 4 short papers included in this book were carefully reviewed and selected from 22 submissions. They were organized in topical sections as follows: answer set programming; functional programming; languages, methods and tools; and declarative solutions.
Machine Learning with Matlab. Unsupervised Learning Techniques
The availability of large volumes of data and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics that includes Multivariate Data Analysis (MDA), Machine Learning, Data Mining and other Business Intelligence techniques. Machine learning uses two types of techniques: Supervised Learning techniques (predictive techniques), which trains a model on known input and output data so that it can predict future outputs, and Supervised Learning techniques (descriptive techniques), which finds hidden patterns or intrinsic structures in input data. Unsupervised learning techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common descriptive technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops classification unsupervised learning techniques
Solutions Architect’s Handbook - Second Edition
Third edition out now with coverage on Generative AI, clean architecture, edge computing, and moreKey Features: Turn business needs into end-to-end technical architectures with this practical guideAssess and overcome various challenges while updating or modernizing legacy applicationsFuture-proof your architecture with IoT, machine learning, and quantum computingBook Description: Becoming a solutions architect requires a hands-on approach, and this edition of the Solutions Architect's Handbook brings exactly that. This handbook will teach you how to create robust, scalable, and fault-tolerant solutions and next-generation architecture designs in a cloud environment. It will also help you build effective product strategies for your business and implement them from start to finish.This new edition features additional chapters on disruptive technologies, such as Internet of Things (IoT), quantum computing, data engineering, and machine learning. It also includes updated discussions on cloud-native architecture, blockchain data storage, and mainframe modernization with public cloud.The Solutions Architect's Handbook provides an understanding of solution architecture and how it fits into an agile enterprise environment. It will take you through the journey of solution architecture design by providing detailed knowledge of design pillars, advanced design patterns, anti-patterns, and the cloud-native aspects of modern software design.By the end of this handbook, you'll have learned the techniques needed to create efficient architecture designs that meet your business requirements.What You Will Learn: Explore the various roles of a solutions architect in the enterprise landscapeImplement key design principles and patterns to build high-performance cost-effective solutionsChoose the best strategies to secure your architectures and increase their availabilityModernize legacy applications with the help of cloud integrationUnderstand how big data processing, machine learning, and IoT fit into modern architectureIntegrate a DevOps mindset to promote collaboration, increase operational efficiency, and streamline productionWho this book is for: This book is for software developers, system engineers, DevOps engineers, architects, and team leaders who already work in the IT industry and aspire to become solutions architect professionals.Existing solutions architects who want to expand their skillset or get a better understanding of new technologies will also learn valuable new skills.To get started, you'll need a good understanding of the real-world software development process and general programming experience in any language.
Computing and Data Science
This volume constitutes selected papers presented at the Third International Conference on Computing and Data Science, CONF-CDS 2021, held online in August 2021. The 22 full papers 9 short papers presented in this volume were thoroughly reviewed and selected from the 85 qualified submissions. They are organized in topical sections on advances in deep learning; algorithms in machine learning and statistics; advances in natural language processing.
Introduction to Data Systems
Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the "data-aptitude" built by the material in this book.
Geospatial Data Analytics and Urban Applications
This book highlights advanced applications of geospatial data analytics to address real-world issues in urban society. With a connected world, we are generating spatial at unprecedented rates which can be harnessed for insightful analytics which define the way we analyze past events and define the future directions. This book is an anthology of applications of spatial data and analytics performed on them for gaining insights which can be used for problem solving in an urban setting. Each chapter is contributed by spatially aware data scientists in the making who present spatial perspectives drawn on spatial big data. The book shall benefit mature researchers and student alike to discourse a variety of urban applications which display the use of machine learning algorithms on spatial big data for real-world problem solving.
Advances of Science and Technology
This two-volume set of LNICST 411 and 412 constitutes the refereed post-conference proceedings of the 9th International Conference on Advancement of Science and Technology, ICAST 2021, which took place in August 2021. Due to COVID-19 pandemic the conference was held virtually. The 80 revised full papers were carefully reviewed and selected from 202 submissions. The papers present economic and technologic developments in modern societies in 7 tracks: Chemical, Food and Bioprocess Engineering; Electrical and Electronics Engineering; ICT, Software and Hardware Engineering; Civil, Water Resources, and Environmental Engineering ICT; Mechanical and Industrial Engineering; Material Science and Engineering; Energy Science, Engineering and Policy.
Advances of Science and Technology
This two-volume set of LNICST 411 and 412 constitutes the refereed post-conference proceedings of the 9th International Conference on Advancement of Science and Technology, ICAST 2021, which took place in August 2021. Due to COVID-19 pandemic the conference was held virtually. The 80 revised full papers were carefully reviewed and selected from 202 submissions. The papers present economic and technologic developments in modern societies in 7 tracks: Chemical, Food and Bioprocess Engineering; Electrical and Electronics Engineering; ICT, Software and Hardware Engineering; Civil, Water Resources, and Environmental Engineering ICT; Mechanical and Industrial Engineering; Material Science and Engineering; Energy Science, Engineering and Policy.
Formalizing Natural Languages
This book constitutes selected revised papers of the 15th International Conference, NooJ 2021, held in Besan癟on, France, in June 2021. Due to the COVID-19 pandemic the conference was held online. NooJ is a linguistic development environment that allows linguists to formalize several levels of linguistic phenomena. NooJ provides linguists with tools to develop dictionaries, regular grammars, context-free grammars, context-sensitive grammars and unrestricted grammars as well as their graphical equivalent to formalize each linguistic phenomenon. The 20 full papers presented were carefully reviewed and selected from 62 submissions. The papers are organized in the following topics: ​ linguistic formalization and analysis, digital humanities and teaching, natural language processing applications.
Enterprise Architecture Function
1. Introduction.- 2. Architecture Function Pattern Language.- 3. Architecture Function: Context.- 4. Architecture Function: Challenge.- 5. Architecture Function: Constitution.- 6. Pattern Catalog.
Artificial Intelligence
This two-volume set LNCS 13069-13070 constitutes selected papers presented at the First CAAI International Conference on Artificial Intelligence, held in Hangzhou, China, in June 2021. Due to the COVID-19 pandemic the conference was partially held online. The 105 papers were thoroughly reviewed and selected from 307 qualified submissions. The papers are organized in topical sections on applications of AI; computer vision; data mining; explainability, understandability, and verifiability of AI; machine learning; natural language processing; robotics; and other AI related topics.
Big Data Analytics
Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics.
Deep Learning with PyTorch Lightning
Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch WrapperKey Features: Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domainsSpeed up your research using PyTorch Lightning by creating new loss functions, networks, and architecturesTrain and build new algorithms for massive data using distributed trainingBook Description: PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time.You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging.By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning.What You Will Learn: Customize models that are built for different datasets, model architectures, and optimizersUnderstand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be builtUse out-of-the-box model architectures and pre-trained models using transfer learningRun and tune DL models in a multi-GPU environment using mixed-mode precisionsExplore techniques for model scoring on massive workloadsDiscover troubleshooting techniques while debugging DL modelsWho this book is for: This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.
Smart and Intelligent Systems
"The more we know about smart and intelligent systems and their use, the more productive organizations can become, and the more quality of life will improve."-Gavriel Salvendy, President Academy of Science, Engineering and Medicine of Florida, University Distinguished Professor University of Central Florida" "Robots, drones, self-driving cars, and personal assistants are only some of the 'intelligent' and 'smart' systems which are populating our world and changing the way we use technology to carry out our everyday activities, bringing about both exciting opportunities for human-technology symbiosis, as well as compelling design and development challenges.　Through a carefully selected choice of chapters, authored by top scientists in the field, this book, edited by Abbas Moallem, sheds light on fundamental aspects of intelligent and smart systems, investigating the role and impact of affective and psychophysiological computing, machine learning, cybersecurity, agent transparency, and human-agent teaming in the shaping of this new interaction paradigm, as well as the human factors involved in their application in critical domains such as health, education, and manufacturing in the emerging technological landscape."-Constantine Stephanidis, Professor of Computer Science, University of Crete, Distinguished member of Foundation for Research and Technology - Hellas (FORTH)　 In today's digital world, the words "smart" and intelligent" are now used to label devices, machinery, systems, and even environments. What is a "smart" system? Is "smart" synonymous with "intelligent"? If not, what does an "intelligent system" mean? Are all smart systems intelligent? This book tries to answer these questions by summarizing the existing research in various areas and providing new research findings. Smart and Intelligent Systems: The Human Elements in Artificial Intelligence, Robotics, and Cybersecurity presents new areas of smart and intelligent system design. It defines smart and intelligent systems, offers a human factors approach, discusses networking applications, and combines the human element with smart and intelligent systems. This book is perfect for engineering students in data sciences and artificial intelligence and practitioners at all levels in the fields of human factors and ergonomics, systems engineering, computer science, software engineering, and robotics. 　
Big Data Analytics
Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics.
Mobile Data Visualization
Mobile Data Visualization is about facilitating access to and understanding of data on mobile devices. Wearable trackers, mobile phones, and tablets are used by millions of people each day to read weather maps, financial charts, or personal health meters. What is required to create e-ffective visualizations for mobile devices? This book introduces key concepts of mobile data visualization and discusses opportunities and challenges from both research and practical perspectives.Mobile Data Visualization is the first book to provide an overview of how to e-ffectively visualize, analyze, and communicate data on mobile devices. Drawing from the expertise, research, and experience of an international range of academics and practitioners from across the domains of Visualization, Human Computer Interaction, and Ubiquitous Computing, the book explores the challenges of mobile visualization and explains how it diff-ers from traditional data visualization. It highlights opportunities for reaching new audiences with engaging, interactive, and compelling mobile content.In nine chapters, this book presents interesting perspectives on mobile data visualization including: how to characterize and classify mobile visualizations; how to interact with them while on the go and with limited attention spans; how to adapt them to various mobile contexts; specific methods on how to design and evaluate them; reflections on privacy, ethical and other challenges, as well as an outlook to a future of ubiquitous visualization. This accessible book is a valuable and rich resource for visualization designers, practitioners, researchers, and students alike.
Mobile Data Visualization
Mobile Data Visualization is about facilitating access to and understanding of data on mobile devices. Wearable trackers, mobile phones, and tablets are used by millions of people each day to read weather maps, financial charts, or personal health meters. What is required to create e-ffective visualizations for mobile devices? This book introduces key concepts of mobile data visualization and discusses opportunities and challenges from both research and practical perspectives.Mobile Data Visualization is the first book to provide an overview of how to e-ffectively visualize, analyze, and communicate data on mobile devices. Drawing from the expertise, research, and experience of an international range of academics and practitioners from across the domains of Visualization, Human Computer Interaction, and Ubiquitous Computing, the book explores the challenges of mobile visualization and explains how it diff-ers from traditional data visualization. It highlights opportunities for reaching new audiences with engaging, interactive, and compelling mobile content.In nine chapters, this book presents interesting perspectives on mobile data visualization including: how to characterize and classify mobile visualizations; how to interact with them while on the go and with limited attention spans; how to adapt them to various mobile contexts; specific methods on how to design and evaluate them; reflections on privacy, ethical and other challenges, as well as an outlook to a future of ubiquitous visualization. This accessible book is a valuable and rich resource for visualization designers, practitioners, researchers, and students alike.
Fundamentals of Quantum Programming in IBM’s Quantum Computers
This textbook introduces major topics that include quantum bits, superposition, entanglement, logic gates, quantum search algorithm, quantum Fourier transform, inverse quantum Fourier transform, Shor's order-finding algorithm and phase estimation. Everyone can write algorithms and programs in the cloud making using IBM's quantum computers that support IBM Q Experience which contains the composer, open quantum assembly language, simulators and real quantum devices. Furthermore, this book teaches you how to use open quantum assembly language to write quantum programs for dealing with complex problems. Through numerous examples and exercises, readers will learn how to write a quantum program with open quantum assembly language for solving any problem from start to complete. This book includes six main chapters: -Quantum Bits and Quantum Gates-learn what quantum bits are, how to declare and measure them, what quantum gates are and how they work on a simulator or a real device in the cloud. -Boolean Algebra and its Applications-learn how to decompose CCNOT gate into six CNOT gates and nine gates of one bit and how to use NOT gates, CNOT gates and CCNOT gates to implement logic operations including NOT, OR, AND, NOR, NAND, Exclusive-OR (XOR) and Exclusive-NOR (XNOR). -Quantum Search Algorithm and its Applications-learn core concepts of quantum search algorithm and how to write quantum programs to implement core concepts of quantum search algorithm for solving two famous NP-complete problems that are the satisfiability problem in n Boolean variables and m clauses and the clique problem in a graph with n vertices and q edges. -Quantum Fourier Transform and its Applications-learn core concepts of quantum Fourier transform and inverse quantum Fourier transform and how to write quantum programs to implement them for solving two real applications that are to compute the period and the frequency of two given oracular functions. -Order-Finding and Factoring-learn core concepts of Shor's order-finding algorithm and how to write quantum programs to implement Shor's order-finding algorithm for completing the prime factorization to 15. Phase Estimation and its Applications-learn core concepts of phase estimation and quantum counting and how to write quantum programs to implement them to compute the number of solution(s) in the independent set problem in a graph with two vertices and one edge.
Heterogeneous Data Management, Polystores, and Analytics for Healthcare
This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2021, and the 7th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2021, which were held virtually on August 2021. For Poly 2021, 7 full and 2 short papers were accepted from 10 submissions; and for DMAH 2021, 4 full papers together with 2 invited papers were accepted from a total of 7 submissions. The papers were organized in topical sections as follows: distributed information systems in enterprises, enterprise access to data constructed from a variety of programming models, data management, data integration, data curation, privacy, and security innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare.
Hands-On Guide to AgileOps
Discover the best practices for transforming cloud and infrastructure operations by using Agile, Scrum, Kanban, Scrumban and Spotify models. This book will help you gain an in-depth understanding of these processes so that you can apply them to your own work. The book begins by offering an overview of current processes and methods used in IT Operations using ITIL and IT4IT. The Authors provide a background of the Agile, Scrum, Kanban, SaFe, Scrumban, and Spotify models used in software development. You'll then gain in-depth guidance and best practices to implement Agile in the Operations world. You'll see how Agile, Site Reliability Engineering and DevOps work in tandem to provide the foundation for modern day infrastructure and cloud operations. The book also offers a comparison of various agile processes and their suitability to the infrastructure and cloud operations world. After completing this is hands-on guide, you'll know how to adopt Agile, DevOps and SRE and select the most suitable processes for your organization to achieve higher reliability, agility and lower costs while running cloud and infrastructure operations. What You Will LearnUnderstand how cloud computing and microservices architecture are changing operations dynamicsUnderstand ITIL, IT4IT, and LeanLearn how Site Reliability Engineering, Agile and DevOps work in tandemLeverage Agile, Scrum, Kanban, Scrumban, and Spotify models to run cloud operationsUse Site Reliability techniques along with Agile and DevOpsStudy the different agile frameworks (Spotify, SAFe, LeSS, DAD, Nexus), their purpose, benefits and implementation approaches.Learn a step-by-step process to identify and implement these frameworks in your organization Who This Book is For Infrastructure architects, DevOps architects, Agile practitioners, DevSecOps Experts, Product Managers/Scrum Masters, DevOps Engineers.
