Cloud Analytics with Microsoft Azure - Second Edition
Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure featuresKey Features: Updated with the latest features and new additions to Microsoft AzureMaster the fundamentals of cloud analytics using AzureLearn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insightsBook Description: Cloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data.The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse.Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts.By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization.What You Will Learn: Explore the concepts of modern data warehouses and data pipelinesDiscover unique design considerations while applying a cloud analytics solutionDesign an end-to-end analytics pipeline on the cloudDifferentiate between structured, semi-structured, and unstructured dataChoose a cloud-based service for your data analytics solutionsUse Azure services to ingest, store, and analyze data of any scaleWho this book is for: This book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure.Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book.
Data Analytics in Marketing, Entrepreneurship, and Innovation
Focusing on important aspects of analytics that facilitate innovation, promote entrepreneurship, and improve marketing efficiency and effectiveness, this book provides researchers and business practitioners insight on the techniques, tools, models, and cases of data analytics in the broad areas of innovation, entrepreneurship, and marketing.
Machine Translation
This book constitutes the refereed proceedings of the 16th China Conference on Machine Translation, CCMT 2020, held in Hohhot, China, in October 2020. The 13 papers presented in this volume were carefully reviewed and selected from 78 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.
High Performance Computing in Science and Engineering
This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on High Performance Computing in Science and Engineering, HPCSE 2019, held in Karolinka, Czech Republic, in May 2019. The 9 papers presented in this volume were carefully reviewed and selected from 13 submissions. The conference provides an international forum for exchanging ideas among researchers involved in scientific and parallel computing, including theory and applications, as well as applied and computational mathematics. The focus of HPCSE 2019 was on models, algorithms, and software tools that facilitate efficient and convenient utilization of modern parallel and distributed computing architectures, as well as on large-scale applications.
Digital Transformation and Global Society
This volume constitutes refereed proceedings of the 5th International Conference on Digital Transformation and Global Society, DTGS 2020, held in St. Petersburg, Russia, in June 2020. Due to the COVID-19 pandemic the conference was held online. The 30 revised full papers and 6 short papers presented in the volume were carefully reviewed and selected from 108 submissions. The papers are organized in topical sections on ​e-society: virtual communities and online activism; e-society: computational social science; e-polity: governance and politics on the Internet; e-city: smart cities and urban governance; e-economy: digital economy and consumer behavior; e-humanities: digital culture and education; e-health: international workshop "E-Health: 4P-medicine & Digital Transformation".
Hands-on MuleSoft Anypoint platform Volume 2
Hands-on MuleSoft Flows using MuleSoft Anypoint Studio Components and understanding Payload processing along with debugging. Key Features Get familiar with the MuleSoft Anypoint Studio key techniques such as Payload, Logger, Variables, Flow and Flow Reference. Deep dive into Massage Structure and Payload value handling. Get familiar with the Global Configuration Properties and Securing properties. Explore Mule Run Time and Deploying Mule Projects in CloudHub. DescriptionThis book is aimed to teach the readers how to design RAML APIs using Anypoint Platform. It also focuses on popular topics such as System Integration, API Led Connectivity, and Centre for Enablement and RAML. It will show how to use, call and test free mock REST APIs. The readers can also work with some commercially available license-based APIs.Furthermore, the book will explain most of the examples provided by RAML.org so that you can simulate it from your local system. This book will then help you develop your RESTful API specification for adding, retrieving, updating and deleting data for a business entity. Later, you will learn how to use the MuleSoft Anypoint Platform Designer for designing and simulating your RAML API design specifications. By the end, you will be able to develop an end to end RAML API using the MuleSoft Anypoint Studio.What you will learn Get exposed to Payload handling, logging and variables Work with different Flow Control components such as Choice, First Successful, Round-Robin and Scatter Gather Explore and work with Error Handling components such as Error Handler, On Error, Continue, On Error Propagate and Raise Error Understand Global Configuration Properties and Securing properties Gain knowledge about various scopes involved in MuleSoft Flow designing Who this book is forThis book is meant for anyone interested to become an API designer. Experienced technical persons of the IT industry also can utilize the book to get extra insights, and they can align their knowledge in line with it.Table of Contents 1. Start Project2. Anypoint Studio Components3. Flow Control Components4. Idempotent, Parse Template and Scheduler5. Payload Component6. MUnit7. MuleSoft Runtime 8. Global Secured Configurations9. Error Handling10. RAML and Anypoint StudioAbout the Authors Nanda Nachimuthu is an Engineering graduate from Tamil Nadu Agricultural University, Coimbatore, and has completed Advanced Diploma from the Indian Institute of Technology, Kharagpur, in the field of Java and Internet Computing. He has also completed an Advanced Diploma from Indian Institute of International Trading, Delhi, which specializes in Strategies for International Business. He has 25 years of experience in various domains like banking, healthcare, government, and airlines. He's into technologies like Java, Big Data, Cloud, ESB, Security and IoT.He has taken up various roles like technical architect, solutions architect, cloud architect, and enterprise integration architect and always wanted to take up an individual contributor's role with hands-on coding experience. He is passionate about social entrepreneurship and takes pro-bono consultations in multiple fields like information technology, manufacturing, trading, agriculture, and internet of things.The founder of some social platforms, he also has a few trademarks under his kitty. Presently, he is focusing on integration technology platforms like MuleSoft, where he finds a wide scope in the future for digital marketing and machine to machine communication.LinkedIn Profile: https: //www.linkedin.com/in/nanda
Identification of Pathogenic Social Media Accounts
This book sheds light on the challenges facing social media in combating malicious accounts, and aims to introduce current practices to address the challenges. It further provides an in-depth investigation regarding characteristics of "Pathogenic Social Media (PSM),"by focusing on how they differ from other social bots (e.g., trolls, sybils and cyborgs) and normal users as well as how PSMs communicate to achieve their malicious goals. This book leverages sophisticated data mining and machine learning techniques for early identification of PSMs, using the relevant information produced by these bad actors. It also presents proactive intelligence with a multidisciplinary approach that combines machine learning, data mining, causality analysis and social network analysis, providing defenders with the ability to detect these actors that are more likely to form malicious campaigns and spread harmful disinformation. Over the past years, social media has played a major role in massive dissemination of misinformation online. Political events and public opinion on the Web have been allegedly manipulated by several forms of accounts including "Pathogenic Social Media (PSM)" accounts (e.g., ISIS supporters and fake news writers). PSMs are key users in spreading misinformation on social media - in viral proportions. Early identification of PSMs is thus of utmost importance for social media authorities in an effort toward stopping their propaganda. The burden falls to automatic approaches that can identify these accounts shortly after they began their harmful activities. Researchers and advanced-level students studying and working in cybersecurity, data mining, machine learning, social network analysis and sociology will find this book useful. Practitioners of proactive cyber threat intelligence and social media authorities will also find this book interesting and insightful, as it presents an important andemerging type of threat intelligence facing social media and the general public.
Zero to Data Viz as a Tableau Desktop Specialist
Zero to Data Viz as a Tableau Desktop Specialist is the full-color guide you need kickstart your career in data visualization and become a certified Tableau Desktop Specialist. Business analysis remains one of the top technical skills for job growth, and Tableau is a leading tool for visual analytics. Zero to Data Viz provides you with relevant context to design outcomes-focused data visualizations, and the required skills to earn your Tableau Desktop Specialist certification. By the end of the book you will: Connect and prepare dataUnderstand Tableau conceptsExplore and analyze data with Tableau charts and analyticsShare your insights with dashboards and storiesHave the knowledge required to pass the Tableau Desktop Specialist ExamYou will learn all of this with the free version of Tableau Public, or you can choose to use the fully licensed Tableau Desktop.
Azure Data Factory Cookbook
Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data FactoryKey FeaturesLearn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory's visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook DescriptionAzure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You'll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you'll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you'll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You'll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF.By the end of this book, you'll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects.What You Will LearnCreate an orchestration and transformation job in ADFDevelop, execute, and monitor data flows using Azure SynapseCreate big data pipelines using Azure Data Lake and ADFBuild a machine learning app with Apache Spark and ADFMigrate on-premises SSIS jobs to ADFIntegrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure FunctionsRun big data compute jobs within HDInsight and Azure DatabricksCopy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectorsWho this book is forThis book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You'll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is expected.
Intelligent Human Computer Interaction
The two-volume set LNCS 12615 + 12616 constitutes the refereed proceedings of the 12th International Conference on Intelligent Human Computer Interaction, IHCI 2020, which took place in Daegu, South Korea, during November 24-26, 2020.The 75 full and 18 short papers included in these proceedings were carefully reviewed and selected from a total of 185 submissions. The papers were organized in topical sections named: cognitive modeling and systems; biomedical signal processing and complex problem solving; natural language, speech, voice and study; algorithms and related applications; crowd sourcing and information analysis; intelligent usability and test system; assistive living; image processing and deep learning; and human-centered AI applications.
Intelligent Human Computer Interaction
The two-volume set LNCS 12615 + 12616 constitutes the refereed proceedings of the 12th International Conference on Intelligent Human Computer Interaction, IHCI 2020, which took place in Daegu, South Korea, during November 24-26, 2020.The 75 full and 18 short papers included in these proceedings were carefully reviewed and selected from a total of 185 submissions. The papers were organized in topical sections named: cognitive modeling and system; biomedical signal processing and complex problem solving; natural language, speech, voice and study; algorithm and related applications; crowd sourcing and information analysis; intelligent usability and test system; assistive living; image processing and deep learning; and human-centered AI applications.
Computational Approaches to the Network Science of Teams
Business operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team's performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.
The Data Book
This book fills a gap by covering the principles of data and information essential for every researcher. These topics are firmly planted in existing graduate curricula, and covered in the research methods courses required of most master's level programs, yet a comprehensive and authoritative text did not exist until now.
Sas(r) Coding Primer and Reference Guide
A primer for learning SAS and analytics quickly, the book makes use of real-world data analysis steps. Filled with line-by-line code explanations, it is packed with fully functional and easily modifiable code covering both data manipulation as well as statistical analyses.
Web Information Systems Engineering - Wise 2020
This book constitutes the proceedings of the 21st International Conference on Web Information Systems Engineering, WISE 2020, held in Amsterdam, The Netherlands, in October 2020.The 81 full papers presented were carefully reviewed and selected from 190 submissions. The papers are organized in the following topical sections: Part I: network embedding; graph neural network; social network; graph query; knowledge graph and entity linkage; spatial temporal data analysis; and service computing and cloud computing Part II: information extraction; text mining; security and privacy; recommender system; database system and workflow; and data mining and applications
Transactions on Rough Sets XXII
The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume XXII in the series is a continuation of a number of research streams that have grown out of the seminal work of Zdzislaw Pawlak during the first decade of the 21st century.
Chatbot Research and Design
This book constitutes the proceedings of the 4th International Workshop on Chatbot Research and Design, CONVERSATIONS 2020, which was held during November 23-24, 2020, hosted by the University of Amsterdam. The conference was planned to take place in Amsterdam, The Netherlands, but changed to an online format due to the COVID-19 pandemic.The 14 papers included in this volume were carefully reviewed and selected from a total of 36 submissions. The papers in the proceedings are structured in four topical groups: Chatbot UX and user perceptions, social and relational chatbots, chatbot applications, and chatbots for customer service. The papers provide new knowledge through empirical, theoretical, or design contributions.
Monotonicity in Logic and Language
Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the Second Interdisciplinary Workshop on Logic, Language, and Meaning, TLLM 2020, held in Tsinghua, China, in December 2020. The 12 full papers together presented were fully reviewed and selected from 40 submissions.Due to COVID-19 the workshop will be held online.The workshop covers a wide range of topics where monotonicity is discussed in the context of logic, causality, belief revision, quantification, polarity, syntax, comparatives, and various semantic phenomena in particular languages.
Implementing Microsoft Azure Architect Technologies AZ-303 Exam Prep and Beyond - Second Edition
Become a certified Azure Architect and learn how to design effective solutions that span compute, security, networking, and developmentKey Features: Discover how you can design and architect powerful and cost-effective solutions on Microsoft AzurePrepare to achieve AZ-303 certification with the help of mock tests and practice questionsEnhance your computing, networking, storage, and security skills to design modern cloud-based solutionsBook Description: From designing solutions on Azure to configuring and managing virtual networks, the AZ-303 certification validates your knowledge and skills for all this and much more. Whether you want to take the certification exam or gain hands-on experience in administering, developing, and architecting Azure solutions, this study guide will help you get started.Divided into four modules, this book systematically takes you through the wide range of concepts and features covered in the AZ-303 exam. The first module demonstrates how to implement and monitor infrastructure. You'll develop the skills required to deploy and manage core Azure components such as virtual machines, networking, storage, and Active Directory (AD). As you progress, you'll build on that knowledge and learn how to create resilient and secure applications before moving on to working with web apps, functions, and containers. The final module will get you up to speed with data platforms such as SQL and Cosmos DB, including how to configure the different high availability options. Finally, you'll solve mock tests and assess yourself with the answers provided to get ready to take the exam with confidence.By the end of this book, you'll have learned the concepts and techniques you need to know to prepare for the AZ-303 exam and design effective solutions on Microsoft Azure.What You Will Learn: Manage Azure subscriptions and resourcesEnsure governance and compliance with policies, roles, and blueprintsBuild, migrate, and protect servers in AzureConfigure, monitor, and troubleshoot virtual networksManage Azure AD and implement multi-factor authenticationConfigure hybrid integration with Azure AD ConnectFind out how you can monitor costs, performance, and securityDevelop solutions that use Cosmos DB and Azure SQL DatabaseWho this book is for: This book is for solution architects and experienced developers who advise stakeholders and translate business requirements into secure, scalable, and reliable solutions. Technical architects interested in learning more about designing cloud solutions will also find this book useful. Prior experience and knowledge of various aspects of IT operations, including networking, security, business continuity, disaster recovery, budgeting, and governance, will assist with understanding the concepts covered in the book.
Smart Cities, Green Technologies and Intelligent Transport Systems
This book includes extended and revised selected papers from the 8th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2019, and the 5th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2019, held in Heraklion, Crete, Greece, in May 2019. The 17 full papers presented during SMARTGREENS and VEHITS 2019 were carefully reviewed and selected from the 134 submissions. The papers present research on advances and applications in the fiels of smart cities, green information and communication technologies, sustainability, energy aware systems and technologies, vehicle technology and intelligent transport systems.
Current Trends in Web Engineering
This book constitutes the thoroughly refereed post-workshop proceedings of the 20th International Conference on Web Engineering, ICWE 2020, held in Helsinki, Finland, in June 2020.*The 4 revised full 4 revised short papers were selected from 10 submissions. The workshops complement the main conference and explore new trends on core topics of Web engineering and provide an open discussion space combining solid theory work with practical on-the-field experience. The workshop committee accepted three workshops for publication in this volume: 1st International Workshop on the Web of Things for Humans (WoT4H 2020), 2nd Semantics and the Web for Transport workshop (Sem4Tra 2020), and 6th International Workshop on Knowledge Discovery on the Web (KDWEB 2020). *The conference was held virtually due to the COVID-19 pandemic.
Practical Microservices with Dapr and .NET
Use the new, enticing, and highly portable event-driven runtime to simplify building resilient and scalable microservices for cloud and edge applicationsKey Features: Build resilient, stateless, and stateful microservice applications that run on the cloud and edgeSolve common distributed systems such as low latency and scaling using any language and frameworkUse real-time and proactive monitoring tools to support a reliable and highly available systemBook Description: Over the last decade, there has been a huge shift from heavily coded monolithic applications to finer, self-contained microservices. Dapr is a new, open source project by Microsoft that provides proven techniques and best practices for developing modern applications. It offers platform-agnostic features for running your applications on public cloud, on-premises, and even on edge devices.This book will help you get to grips with microservice architectures and how to manage application complexities with Dapr in no time. You'll understand how Dapr offers ease of implementation while allowing you to work with multiple languages and platforms. You'll also understand how Dapr's runtime, services, building blocks, and software development kits (SDKs) help you to simplify the creation of resilient and portable microservices. Dapr provides an event-driven runtime that supports the essential features you need to build microservices, including service invocation, state management, and publish/subscribe messaging. You'll explore all of those in addition to various other advanced features with this practical guide to learning Dapr.By the end of this book, you'll be able to write microservices easily using your choice of language or framework by implementing industry best practices to solve problems related to distributed systems.What You Will Learn: Use Dapr to create services, invoking them directly and via pub/subDiscover best practices for working with microservice architecturesLeverage the actor model to orchestrate data and behaviorUse Azure Kubernetes Service to deploy a sample applicationMonitor Dapr applications using Zipkin, Prometheus, and GrafanaScale and load test Dapr applications on KubernetesWho This Book Is For: This book is for developers looking to explore microservices architectures and implement them in Dapr applications using examples on Microsoft .NET Core. Whether you are new to microservices or have knowledge of this architectural approach and want to get hands-on experience in using Dapr, you'll find this book useful. Familiarity with .NET Core will help you to understand the C# samples and code snippets used in the book.
Google Data Studio for Beginners
Google Data Studio is becoming a go-to tool in the analytics community. All business roles across the industry benefit from foundational knowledge of this now-essential technology, and Google Data Studio for Beginners is here to provide it. Release your locked-up data and turn it into beautiful, actionable, and shareable reports that can be consumed by experts and novices alike.Authors Grant Kemp and Gerry White begin by walking you through the basics, such how to create simple dashboards and interactive visualizations. As you progress through Google Data Studio for Beginners, you will build up the knowledge necessary to blend multiple data sources and create comprehensive marketing dashboards. Some intermediate features such as calculated fields, cleaning up data, and data blending to build powerhouse reports are featured as well. Presenting your data in client-ready, digestible forms is a key factor that many find to be a roadblock, and this book will help strengthen this essential skill in your organization. Centralizing the power from sources such as Google Analytics, online surveys, and a multitude of other popular data management tools puts you as a business leader and analyzer ahead of the rest. Your team as a whole will benefit from Google Data Studio for Beginners, because by using these tools, teams can collaboratively work on data to build their understanding and turn their data into action. Data Studio is quickly solidifying itself as the industry standard, and you don't want to miss this essential guide for excelling in it. What You Will Learn Combine various data sources to create great looking and actionable visualizationsReuse and modify other dashboards that have been created by industry prosUse intermediate features such as calculated fields and data blending to build powerhouse reports Who This Book Is For Users looking to learn Google Analytics, SEO professionals, digital marketers, and other business professionals who want to mine their data into an actionable dashboard.
Pro Google Cloud Automation
Discover the methodologies and best practices for getting started with Google cloud automation services including Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins to automate deployment of cloud infrastructure and applications. The book begins with an introduction to Google cloud services and takes you through the various platforms available to do automation on the GCP platform. You will do hands-on exercises and see best practices for using Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins. You'll cover the automation aspects of the Google Cloud Platform holistically using native and upcoming open source technologies. The authors cover the entire spectrum of automation from cloud infrastructure to application deployment and tie everything together in a release pipeline using Jenkins. Pro Google Cloud Automation provides in-depth guidance on automation and deployment of microservices-based applications running on the Kubernetes platform. It provides sample code and best practice guidance for developers and architects for their automation projects on the Google Cloud Platform. This book is a good starting point for developers, architects, and administrators who want to learn about Google cloud automation. What You Will Learn Gain the fundamentals of Google's automation-enabling servicesSee an architecture overview for Google Cloud Deployment Manager, Spinnaker, Tekton, and JenkinsImplement automation for infrastructure and application use casesAutomate microservices-based applications running on GKEEnable Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins Who This Book Is For Developers, architects, and administrators who want to learn about Google cloud automation.
Python Data Cleaning Cookbook
Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricksKey featuresGet well-versed with various data cleaning techniques to reveal key insightsManipulate data of different complexities to shape them into the right form as per your business needsClean, monitor, and validate large data volumes to diagnose problems before moving on to data analysisBook DescriptionGetting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data.By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.What you will learnFind out how to read and analyze data from a variety of sourcesProduce summaries of the attributes of data frames, columns, and rowsFilter data and select columns of interest that satisfy given criteriaAddress messy data issues, including working with dates and missing valuesImprove your productivity in Python pandas by using method chainingUse visualizations to gain additional insights and identify potential data issuesEnhance your ability to learn what is going on in your dataBuild user-defined functions and classes to automate data cleaningWho this book is forThis book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.
Advanced Hybrid Information Processing
This two-volume set constitutes the post-conference proceedings of the 4th EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2020, held in Binzhou, China, in September 2020. Due to COVID-19 the conference was held virtually. The 89 papers presented were selected from 190 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 "Industrial applications of aspects with big data". The papers are named in topical sections as follows: Industrial application of multi-modal information processing; Industrialized big data processing; Industrial automation and intelligent control; Visual information processing.
Advanced Hybrid Information Processing
This two-volume set constitutes the post-conference proceedings of the 4th EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2020, held in Binzhou, China, in September 2020. Due to COVID-19 the conference was held virtually. The 89 papers presented were selected from 190 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 "Industrial applications of aspects with big data". The papers are named in topical sections as follows: Industrial application of multi-modal information processing; Industrialized big data processing; Industrial automation and intelligent control; Visual information processing.
Adopting Blockchain and Cryptocurrency
Over the last few years, you may have heard a buzz going on about cryptocurrency. Bitcoin, the most notable of all cryptocurrencies, is slowly becoming a household topic. Blockchain, on the other hand, you may not have heard of unless you're in the tech industry. But what is a blockchain? And how does it relate to cryptocurrency? In Adopting Blockchain and Cryptocurrency, we'll look at use cases for blockchain technology and explore how bitcoin and digital currencies will revolutionize the way we think about conventional banking. The world as we know it is changing. Will you be ready?
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.
Machine Learning and Knowledge Discovery in Databases
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
Machine Learning and Knowledge Discovery in Databases
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
Machine Learning and Knowledge Discovery in Databases
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
Web Information Systems Engineering - Wise 2020
This book constitutes the proceedings of the 21st International Conference on Web Information Systems Engineering, WISE 2020, held in Amsterdam, The Netherlands, in October 2020.The 81 full papers presented were carefully reviewed and selected from 190 submissions. The papers are organized in the following topical sections: Part I: network embedding; graph neural network; social network; graph query; knowledge graph and entity linkage; spatial temporal data analysis; and service computing and cloud computing Part II: information extraction; text mining; security and privacy; recommender system; database system and workflow; and data mining and applications
Machine Learning for Time Series Forecasting with Python
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
Custom Fiori Applications in SAP Hana
Get started building custom Fiori applications for your enterprise. This book teaches you how to design, build, and deploy enterprise-ready, custom Fiori applications in SAP HANA. Tips and tricks collected from projects using Fiori applications (built consuming OData models and REST APIs) and integrating third-party JS libraries are presented. Also included are examples using Fiori templates from different tools such as the SAP Web IDE and the new Visual Studio Code extensions. This book explains the 5 design principles that all Fiori applications are built upon: Role-based, Responsive, Coherent, Simple, and Delightful. The book expands on consuming OData services and REST APIs internal and external to SAP HANA. The Fiori application exercise demonstrates the use of the MVC pattern, JavaScript modularization, reuse of SAP UI5 controls, debugging, and the tools required for a complete scenario. The book closes with an exercise showcasing a finished single page application with multiple views and layouts, navigation between the views, and deployment of the application to AWS. This book is simple enough for entry-level developers getting started in web frameworks but also highlights integration points from the data models being consumed from the application, and shows how the application communicates with back-end services, resulting in a complete front-end custom Fiori application.What You Will LearnKnow the 5 Fiori design principlesUnderstand how to consume OData and REST API modelsApply the MVC pattern using XML views and the SAP UI5 controls along with controller behavior in JavaScriptDebug and deploy the applicationWho This Book is ForWeb developers and application leads who have some experience in JavaScript frameworks and web development and understand web protocol communication
Data Science and Analytics
Data Science and Analytics explores the solutions to problems in society, environment and in industry. With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. This book explores perspectives on how big data and business analytics are increasingly essential in better decision making. This edited work explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. Data Science and Analytics brings together researchers, engineers and practitioners to encompass a wide and diverse range of topics in a wide range of fields. The book will provide unique insights to researchers, academics and data scientists from a variety of disciplines interested in analyzing and application of big data analytics, as well as data analysts, students and scholars pursuing advanced study in big data.
Digital Libraries at Times of Massive Societal Transition
This book constitutes the refereed proceedings of the 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, which was planned to be held in Kyoto, Japan, in November/December 2020, but it was held virtually due to the COVID-19 pandemic.The 10 full, 15 short, 4 practitioners, and 10 work-in-progress papers presented in this volume were carefully reviewed and selected from 79 submissions. The papers were organized in topical sections named: natural language processing; knowledge structures; citation data analysis; user analytics; application of cultural and historical data; social media; metadata and infrastructure; and scholarly data mining.
Augmented IntelligenceThe Business Power of Human-Machine Collaboration
In this book the authors explain how combining human judgment with machine learning can lead to better outcomes for your business. You learn about advances in machine learning and which techniques are most appropriate to solve your business problem. You gain insight into managing the cycle of data management in order to build revenue.
Enterprise Applications, Markets and Services in the Finance Industry
This book constitutes the revised selected papers from the 10th International Workshop on Enterprise Applications, Markets and Services in the Finance Industry, FinanceCom 2020, held in Helsinki, Finland, in August 2020. Due to the COVID-19 pandemic the conference took place virtually.The 6 full papers presented together with 1 extended abstract in this volume were carefully reviewed and selected from a total of 14 submissions to the workshop.They are grouped in topical sections named Machine Learning Applications in Trading and Financial Markets, Fraud Detection and Information Generation in Finance, and Alternative Trading and Investment Offerings by FinTechs.The workshop spans multiple disciplines, including analytical, technical, service, economic, sociological and behavioral sciences.
Business Process Management Forum
This book constitutes the proceedings of the BPM Forum of the 18th International Conference on Business Process Management, BPM 2020, which was planned to take place in Seville, Spain, in September 2020. Due to the COVID-19 pandemic the conference took place virtually. The BPM Forum hosts innovative research which has a high potential of stimulating discussions. The papers selected for the forum are expected to showcase fresh ideas from exciting and emerging topics in BPM, even if they are not yet as mature as the regular papers at the conference. The 19 papers presented in this volume were carefully reviewed and selected from a total of 125 submissions to the main conference. They were organized in topical sections named: process modeling; process mining; predictions and recommendations; BPM adoption and maturity; and standardization, change, and handoffs.
Ai-Powered Iot for Covid-19
This book is dedicated to addressing the major challenges in fighting COVID-19 using AI and IoT technologies.
The Economics of Data, Analytics, and Digital Transformation
Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine LearningKey featuresMaster the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindsetAcquire implementable knowledge on digital transformation through 8 practical lawsExplore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctlyBook DescriptionIn today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise.The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning.By the end of the book, you will have the tools and techniques to drive your organization's digital transformation.Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon."What you will learnTrain your organization to transition from being data-driven to being value-drivenNavigate and master the big data business model maturity indexLearn a methodology for determining the economic value of your data and analyticsUnderstand how AI and machine learning can create analytics assets that appreciate in value the more that they are usedBecome aware of digital transformation misconceptions and pitfallsCreate empowered and dynamic teams that fuel your organization's digital transformationWho this book is forThis book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.
From Parallel to Emergent Computing
From Parallel to Emergent Computing provides a visionary statement on how computing will advance in the next 25 years and what new fields of science will be involved in computing engineering. This book is a valuable resource for computer scientists working today, and in years to come.
Trends in Computational Intelligence, Security and Internet of Things
This volume constitutes the refereed proceedings of the Third International Conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2020, held in Agartala, India, in December 2020. Due to the COVID-19 pandemic the conference was held online. The 23 full papers and 4 short papers were carefully reviewed and selected from 113 submissions. The papers are organised according to the following topics: computational intelligence, security, and internet of things.
