Designing and Building Enterprise Knowledge Graphs
This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice.\ It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people. The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies. Knowledge graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale. Tech giants have adopted knowledge graphs for the foundation of next-generation enterprise data and metadata management, search, recommendation, analytics, intelligent agents, and more. We are now observing an increasing number of enterprises that seek to adopt knowledge graphs to develop a competitive edge. In order for enterprises to design and build knowledge graphs, they need to understand the critical data stored in relational databases. How can enterprises successfully adopt knowledge graphs to integrate data and knowledge, without boiling the ocean? This book provides the answers.
The Maximum Consensus Problem
Outlier-contaminated data is a fact of life in computer vision. For computer vision applications to perform reliably and accurately in practical settings, the processing of the input data must be conducted in a robust manner. In this context, the maximum consensus robust criterion plays a critical role by allowing the quantity of interest to be estimated from noisy and outlier-prone visual measurements. The maximum consensus problem refers to the problem of optimizing the quantity of interest according to the maximum consensus criterion. This book provides an overview of the algorithms for performing this optimization. The emphasis is on the basic operation or "inner workings" of the algorithms, and on their mathematical characteristics in terms of optimality and efficiency. The applicability of the techniques to common computer vision tasks is also highlighted. By collecting existing techniques in a single article, this book aims to trigger further developments in this theoretically interesting and practically important area.
Data Profiling
Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More complex types of metadata are statements about multiple columns and their correlation, such as candidate keys, functional dependencies, and other types of dependencies. This book provides a classification of the various types of profilable metadata, discusses popular data profiling tasks, and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relational data such as graphs and text. We conclude with a discussion of data profiling challenges and directions for future work in this area.
Data Engineering with Alteryx
Build and deploy data pipelines with Alteryx by applying practical DataOps principlesKey Features: Learn DataOps principles to build data pipelines with AlteryxBuild robust data pipelines with Alteryx DesignerUse Alteryx Server and Alteryx Connect to share and deploy your data pipelinesBook Description: Alteryx is a GUI-based development platform for data analytic applications.Data Engineering with Alteryx will help you leverage Alteryx's code-free aspects which increase development speed while still enabling you to make the most of the code-based skills you have.This book will teach you the principles of DataOps and how they can be used with the Alteryx software stack. You'll build data pipelines with Alteryx Designer and incorporate the error handling and data validation needed for reliable datasets. Next, you'll take the data pipeline from raw data, transform it into a robust dataset, and publish it to Alteryx Server following a continuous integration process.By the end of this Alteryx book, you'll be able to build systems for validating datasets, monitoring workflow performance, managing access, and promoting the use of your data sources.What You Will Learn: Build a working pipeline to integrate an external data sourceDevelop monitoring processes for the pipeline exampleUnderstand and apply DataOps principles to an Alteryx data pipelineGain skills for data engineering with the Alteryx software stackWork with spatial analytics and machine learning techniques in an Alteryx workflow Explore Alteryx workflow deployment strategies using metadata validation and continuous integrationOrganize content on Alteryx Server and secure user accessWho this book is for: If you're a data engineer, data scientist, or data analyst who wants to set up a reliable process for developing data pipelines using Alteryx, this book is for you. You'll also find this book useful if you are trying to make the development and deployment of datasets more robust by following the DataOps principles. Familiarity with Alteryx products will be helpful but is not necessary.
Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators
Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators deals with the modeling and control of parallel robots. The book's content will benefit students, researchers and engineers in robotics by providing a simplified methodology to obtain the dynamic model of parallel robots with a delta-type architecture. Moreover, this methodology is compatible with the real-time implementation of model-based and robust control schemes. And, it can easily extend the proposed robust control solutions to other robotic architectures.
Learning Google Analytics
Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Rather than simply reporting what has happened, GA4's new cloud integrations enable more data activation, linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations. Author Mark Edmondson, Google developer expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get the guidance you need to implement them. You'll learn: How Google Cloud integrates with GA4 The potential use cases that GA4 integrations can enable Skills and resources needed to create GA4 integrations How much GA4 data capture is necessary to enable use cases The process of designing dataflows from strategy through data storage, modeling, and activation How to adapt the use cases to fit your business needs
Blockchain Technology for Emerging Applications
Blockchain Technology for Emerging Applications: A Comprehensive Approach explores recent theories and applications of the execution of blockchain technology. Chapters look at a wide range of application areas, including healthcare, digital physical frameworks, web of-things, smart transportation frameworks, interruption identification frameworks, ballot-casting, architecture, smart urban communities, and digital rights administration. The book addresses the engineering, plan objectives, difficulties, constraints, and potential answers for blockchain-based frameworks. It also looks at blockchain-based design perspectives of these intelligent architectures for evaluating and interpreting real-world trends. Chapters expand on different models which have shown considerable success in dealing with an extensive range of applications, including their ability to extract complex hidden features and learn efficient representation in unsupervised environments for blockchain security pattern analysis.
Wearable Sensing and Intelligent Data Analysis for Respiratory Management
Wearable Sensing and Intelligent Data Analysis for Respiratory Management highlights the use of wearable sensing and intelligent data analysis algorithms for respiratory function management, offering several potential and substantial clinical benefits. The book allows for the early detection of respiratory exacerbations in patients with chronic respiratory diseases, allowing earlier and, therefore, more effective treatment. As such, the problem of continuous, non-invasive, remote and real-time monitoring of such patients needs increasing attention from the scientific community as these systems have the potential for substantial clinical benefits, promoting P4 medicine (personalized, participative, predictive and preventive). Wearable and portable systems with sensing technology and automated analysis of respiratory sounds and pulmonary images are some of the problems that are the subject of current research efforts, hence this book is an ideal resource on the topics discussed.
Data Democratization with Domo
Overcome data challenges at record speed and cloud-scale that optimize businesses by transforming raw data into dashboards and apps which democratize data consumption, supercharging results with the cloud-based solution, DomoKey Features: Acquire data and automate data pipelines quickly for any data volume, variety, and velocityPresent relevant stories in dashboards and custom apps that drive favorable outcomes using DomoShare information securely and govern content including Domo content embedded in other toolsBook Description: Domo is a power-packed business intelligence (BI) platform that empowers organizations to track, analyze, and activate data in record time at cloud scale and performance.Data Democratization with Domo begins with an overview of the Domo ecosystem. You'll learn how to get data into the cloud with Domo data connectors and Workbench; profile datasets; use Magic ETL to transform data; work with in-memory data sculpting tools (Data Views and Beast Modes); create, edit, and link card visualizations; and create card drill paths using Domo Analyzer. Next, you'll discover options to distribute content with real-time updates using Domo Embed and digital wallboards. As you advance, you'll understand how to use alerts and webhooks to drive automated actions. You'll also build and deploy a custom app to the Domo Appstore and find out how to code Python apps, use Jupyter Notebooks, and insert R custom models. Furthermore, you'll learn how to use Auto ML to automatically evaluate dozens of models for the best fit using SageMaker and produce a predictive model as well as use Python and the Domo Command Line Interface tool to extend Domo. Finally, you'll learn how to govern and secure the entire Domo platform.By the end of this book, you'll have gained the skills you need to become a successful Domo master.What You Will Learn: Understand the Domo cloud data warehouse architecture and platformAcquire data with Connectors, Workbench, and Federated QueriesSculpt data using no-code Magic ETL, Data Views, and Beast ModesProfile data with the Data Dictionary, Data Profile, and Usage toolsUse a storytelling pattern to create dashboards with Domo StoriesCreate, share, and monitor custom alerts activated using webhooksCreate custom Domo apps, use the Domo CLI, and code with the Python APIAutomate model operations with Python programming and R scriptingWho this book is for: This book is for BI developers, ETL developers, and Domo users looking for a comprehensive, end-to-end guide to exploring Domo features for BI. Chief data officers, data strategists, architects, and BI managers interested in a new paradigm for integrated cloud data storage, data transformation, storytelling, content distribution, custom app development, governance, and security will find this book useful. Business analysts seeking new ways to tell relevant stories to shape business performance will also benefit from this book. A basic understanding of Domo will be helpful.
The DevOps Career Handbook
Explore the diverse DevOps career paths and prepare for each stage of the interview process with collective wisdom from DevOps experts and interviews with DevOps PractitionersKey Features: Navigate the many career opportunities in the field of DevOpsDiscover proven tips and tricks from industry experts for every step of the DevOps interviewSave both time and money by avoiding common mistakes in your interviewsBook Description: DevOps is a set of practices that make up a culture, and practicing DevOps methods can make developers more productive and easier to work with. The DevOps Career Handbook is filled with hundreds of tips and tricks from experts regarding every step of the interview process, helping you save time and money by steering clear of avoidable mistakes.You'll learn about the various career paths available in the field of DevOps, before acquiring the essential skills needed to begin working as a DevOps professional. If you are already a DevOps engineer, this book will help you to gain advanced skills to become a DevOps specialist. After getting to grips with the basics, you'll discover tips and tricks for preparing your resume and online profiles and find out how to build long-lasting relationships with the recruiters. Finally, you'll read through interviews which will give you an insight into a career in DevOps from the viewpoint of individuals at different career levels.By the end of this DevOps book, you'll gain a solid understanding of what DevOps is, the various DevOps career paths, and how to prepare for your interview.What You Will Learn: Understand various roles and career paths for DevOps practitionersDiscover proven techniques to stand out in the application processPrepare for the many stages of your interview, from the phone screen to taking the technical challenge and then the onsite interviewNetwork effectively to help your career move in the right directionTailor your resume to specific DevOps rolesDiscover how to negotiate after you've been extended an offerWho this book is for: This book is for DevOps professionals looking to take the next step in their career, engineers looking to make a career switch, technology managers who want to understand the complete picture of the DevOps landscape, and anyone interested in incorporating DevOps into their tech journey.
Logic and Language Models for Computer Science (Fourth Edition)
This unique compendium highlights the theory of computation, particularly logic and automata theory. Special emphasis is on computer science applications including loop invariants, program correctness, logic programming and algorithmic proof techniques.This innovative volume differs from standard textbooks, by building on concepts in a different order, using fewer theorems with simpler proofs. It has added many new examples, problems and answers. It can be used as an undergraduate text at most universities.
Elasticsearch 8.x Cookbook - Fifth Edition
Search, analyze, store and manage data effectively with Elasticsearch 8.xKey Features: Explore the capabilities of Elasticsearch 8.x with easy-to-follow recipesExtend the Elasticsearch functionalities and learn how to deploy on Elastic CloudDeploy and manage simple Elasticsearch nodes as well as complex cluster topologiesBook Description: Elasticsearch is a Lucene-based distributed search engine at the heart of the Elastic Stack that allows you to index and search unstructured content with petabytes of data. With this updated fifth edition, you'll cover comprehensive recipes relating to what's new in Elasticsearch 8.x and see how to create and run complex queries and analytics.The recipes will guide you through performing index mapping, aggregation, working with queries, and scripting using Elasticsearch. You'll focus on numerous solutions and quick techniques for performing both common and uncommon tasks such as deploying Elasticsearch nodes, using the ingest module, working with X-Pack, and creating different visualizations. As you advance, you'll learn how to manage various clusters, restore data, and install Kibana to monitor a cluster and extend it using a variety of plugins. Furthermore, you'll understand how to integrate your Java, Scala, Python, and big data applications such as Apache Spark and Pig with Elasticsearch and create efficient data applications powered by enhanced functionalities and custom plugins.By the end of this Elasticsearch cookbook, you'll have gained in-depth knowledge of implementing the Elasticsearch architecture and be able to manage, search, and store data efficiently and effectively using Elasticsearch.What You Will Learn: Become well-versed with the capabilities of X-PackOptimize search results by executing analytics aggregationsGet to grips with using text and numeric queries as well as relationship and geo queriesInstall Kibana to monitor clusters and extend it for pluginsBuild complex queries by managing indices and documentsMonitor the performance of your cluster and nodesDesign advanced mapping to take full control of index stepsIntegrate Elasticsearch in Java, Scala, Python, and big data applicationsWho this book is for: If you're a software engineer, big data infrastructure engineer, or Elasticsearch developer, you'll find this Elasticsearch book useful. The book will also help data professionals working in e-commerce and FMCG industries who use Elastic for metrics evaluation and search analytics to gain deeper insights and make better business decisions. Prior experience with Elasticsearch will help you get the most out of this book.
Data Science on the Google Cloud Platform
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines
Secure Data Science
Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area--including healthcare, finance, manufacturing, and marketing--could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science.After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media.This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.
Azure Synapse Analytics Cookbook
Whether you're an Azure veteran or just getting started, get the most out of your data with effective recipes for Azure SynapseKey Features: Discover new techniques for using Azure Synapse, regardless of your level of expertiseIntegrate Azure Synapse with other data sources to create a unified experience for your analytical needs using Microsoft AzureLearn how to embed data governance and classification with Synapse Analytics by integrating Azure PurviewBook Description: As data warehouse management becomes increasingly integral to successful organizations, choosing and running the right solution is more important than ever. Microsoft Azure Synapse is an enterprise-grade, cloud-based data warehousing platform, and this book holds the key to using Synapse to its full potential. If you want the skills and confidence to create a robust enterprise analytical platform, this cookbook is a great place to start.You'll learn and execute enterprise-level deployments on medium-to-large data platforms. Using the step-by-step recipes and accompanying theory covered in this book, you'll understand how to integrate various services with Synapse to make it a robust solution for all your data needs. Whether you're new to Azure Synapse or just getting started, you'll find the instructions you need to solve any problem you may face, including using Azure services for data visualization as well as for artificial intelligence (AI) and machine learning (ML) solutions.By the end of this Azure book, you'll have the skills you need to implement an enterprise-grade analytical platform, enabling your organization to explore and manage heterogeneous data workloads and employ various data integration services to solve real-time industry problems.What You Will Learn: Discover the optimal approach for loading and managing dataWork with notebooks for various tasks, including MLRun real-time analytics using Azure Synapse Link for Cosmos DBPerform exploratory data analytics using Apache SparkRead and write DataFrames into Parquet files using PySparkCreate reports on various metrics for monitoring key KPIsCombine Power BI and Serverless for distributed analysisEnhance your Synapse analysis with data visualizationsWho this book is for: This book is for data architects, data engineers, and developers who want to learn and understand the main concepts of Azure Synapse analytics and implement them in real-world scenarios.
Metadata Matters
"In what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata matters-he vividly illustrates why it matters." -Patricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San Jos矇 State University, USA If data is the language upon which our modern society will be built, then metadata will be its grammar, the construction of its meaning, the building for its content, and the ability to understand what data can be for us all. We are just starting to bring change into the management of the data that connects our experiences. Metadata Matters explains how metadata is the foundation of digital strategy. If digital assets are to be discovered, they want to be found. The path to good metadata design begins with the realization that digital assets need to be identified, organized, and made available for discovery. This book explains how metadata will help ensure that an organization is building the right system for the right users at the right time. Metadata matters and is the best chance for a return on investment on digital assets and is also a line of defense against lost opportunities. It matters to the digital experience of users. It helps organizations ensure that users can identify, discover, and experience their brands in the ways organizations intend. It is a necessary defense, which this book shows how to build.
Metadata Matters
"In what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata matters-he vividly illustrates why it matters." -Patricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San Jos矇 State University, USA If data is the language upon which our modern society will be built, then metadata will be its grammar, the construction of its meaning, the building for its content, and the ability to understand what data can be for us all. We are just starting to bring change into the management of the data that connects our experiences. Metadata Matters explains how metadata is the foundation of digital strategy. If digital assets are to be discovered, they want to be found. The path to good metadata design begins with the realization that digital assets need to be identified, organized, and made available for discovery. This book explains how metadata will help ensure that an organization is building the right system for the right users at the right time. Metadata matters and is the best chance for a return on investment on digital assets and is also a line of defense against lost opportunities. It matters to the digital experience of users. It helps organizations ensure that users can identify, discover, and experience their brands in the ways organizations intend. It is a necessary defense, which this book shows how to build.
Advanced Research in VLSI
The field of VLSI (Very Large Scale Integration) is concerned with the design, production, and use of highly complex integrated circuits. The research collected here comes from many disciplines, including computer architecture, computer-aided design, parallel algorithms, semiconductor technology, and testing. It extends to novel uses of the technology and concepts originally developed for integrated circuits, including integrated sensor arrays, digital photography, highly parallel computers, microactuators, neural networks, and a variety of special-purpose architectures and networks of special-purpose devices.
Evolutionary Computation in Combinatorial Optimization
This book constitutes the refereed proceedings of the 22nd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2022, held as part of Evo*2022, in Madrid, Spain, during April 20-21, 2022, co-located with the Evo*2022 events: EvoMUSART, EvoApplications, and EuroGP.The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.
5g Iot and Edge Computing for Smart Healthcare
5G IoT and Edge Computing for Smart Healthcare addresses the importance of a 5G IoT and Edge-Cognitive-Computing-based system for the successful implementation and realization of a smart-healthcare system. The book provides insights on 5G technologies, along with intelligent processing algorithms/processors that have been adopted for processing the medical data that would assist in addressing the challenges in computer-aided diagnosis and clinical risk analysis on a real-time basis. Each chapter is self-sufficient, solving real-time problems through novel approaches that help the audience acquire the right knowledge. With the progressive development of medical and communication - computer technologies, the healthcare system has seen a tremendous opportunity to support the demand of today's new requirements.
Fintech Policy Tool Kit for Regulators and Policy Makers in Asia and the Pacific
This tool kit provides insights on how new fintech solutions, aided by strong policy and regulation, can support more inclusive growth and help economies recover from the pandemic.The rapid growth of fintech services in Asia and the Pacific can help countries leapfrog the challenges of traditional financial services infrastructure and dramatically increase access to financial services. An inclusive fintech ecosystem is important in supporting economic growth, greater equality, and lower poverty levels. This publication suggests how to provide an enabling policy and regulatory environment to promote responsible fintech innovation, while ensuring consumer protection and supporting inclusive economic development in the region.
Essential Mathematics for Quantum Computing
Demystify quantum computing by learning the math it is built onKey Features: - Build a solid mathematical foundation to get started with developing powerful quantum solutions- Understand linear algebra, calculus, matrices, complex numbers, vector spaces, and other concepts essential for quantum computing- Learn the math needed to understand how quantum algorithms functionBook Description: Quantum computing is an exciting subject that offers hope to solve the world's most complex problems at a quicker pace. It is being used quite widely in different spheres of technology, including cybersecurity, finance, and many more, but its concepts, such as superposition, are often misunderstood because engineers may not know the math to understand them. This book will teach the requisite math concepts in an intuitive way and connect them to principles in quantum computing.Starting with the most basic of concepts, 2D vectors that are just line segments in space, you'll move on to tackle matrix multiplication using an instinctive method. Linearity is the major theme throughout the book and since quantum mechanics is a linear theory, you'll see how they go hand in hand. As you advance, you'll understand intrinsically what a vector is and how to transform vectors with matrices and operators. You'll also see how complex numbers make their voices heard and understand the probability behind it all.It's all here, in writing you can understand. This is not a stuffy math book with definitions, axioms, theorems, and so on. This book meets you where you're at and guides you to where you need to be for quantum computing. Already know some of this stuff? No problem! The book is componentized, so you can learn just the parts you want. And with tons of exercises and their answers, you'll get all the practice you need.What You Will Learn: - Operate on vectors (qubits) with matrices (gates)- Define linear combinations and linear independence- Understand vector spaces and their basis sets- Rotate, reflect, and project vectors with matrices- Realize the connection between complex numbers and the Bloch sphere- Determine whether a matrix is invertible and find its eigenvalues- Probabilistically determine the measurement of a qubit- Tie it all together with bra-ket notationWho this book is for: If you want to learn quantum computing but are unsure of the math involved, this book is for you. If you've taken high school math, you'll easily understand the topics covered. And even if you haven't, the book will give you a refresher on topics such as trigonometry, matrices, and vectors. This book will help you gain the confidence to fully understand quantum computation without losing you in the process!Table of Contents- Superposition with Euclid - The Matrix- Foundations- Vector Spaces- Using Matrices to Transform Space- Complex Numbers- Eigenstuff- Our Space in the Universe- Advanced Concepts- Appendix 1 - Bra-ket Notation- Appendix 2 - Sigma Notation- Appendix 3 - Trigonometry- Appendix 4 - Probability- Appendix 5 - References
Scalable Data Analytics with Azure Data Explorer
Write efficient and powerful KQL queries to query and visualize your data and implement best practices to improve KQL execution performanceKey Features: Apply Azure Data Explorer best practices to manage your data at scale and reduce KQL execution timeDiscover how to query and visualize your data using the powerful KQLManage cluster performance and monthly costs by understanding how to size your ADX cluster correctlyBook Description: Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters.The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance.By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.What You Will Learn: Become well-versed with the core features of the Azure Data Explorer architectureDiscover how ADX can help manage your data at scale on AzureGet to grips with deploying your ADX environment and ingesting and analyzing your dataExplore KQL and learn how to query your dataQuery and visualize your data using the ADX UI and Power BIIngest structured and unstructured data types from an array of sourcesUnderstand how to deploy, scale, secure, and manage ADXWho this book is for: This book is for data analysts, data engineers, and data scientists who are responsible for analyzing and querying their team's large volumes of data on Azure. SRE and DevOps engineers who deploy, maintain, and secure infrastructure will also find this book useful. Prior knowledge of Azure and basic data querying will help you to get the most out of this book.
Computer Models of Process Dynamics
COMPUTER MODELS OF PROCESS DYNAMICS Comprehensive overview of techniques for describing physical phenomena by means of computer models that are determined by mathematical analysis Computer Models of Process Dynamics covers everything required to do computer based mathematical modeling of dynamic systems, including an introduction to a scientific language, its use to program essential operations, and methods to approximate the integration of continuous signals. From a practical standpoint, readers will learn how to build computer models that simulate differential equations. They are also shown how to model physical objects of increasing complexity, where the most complex objects are simulated by finite element models, and how to follow a formal procedure in order to build a valid computer model. To aid in reader comprehension, a series of case studies is presented that covers myriad different topics to provide a view of the challenges that fall within this discipline. The book concludes with a discussion of how computer models are used in an engineering project where the readers would operate in a team environment. Other topics covered in Computer Models of Process Dynamics include: Computer hardware and software, covering algebraic expressions, math functions, computation loops, decision-making, graphics, and user-defined functions Creative thinking and scientific theories, covering the Ancients, the Renaissance, Galileo, Newton, electricity and magnetism, and newer sciences Uncertainty and softer science, covering random number generators, statistical analysis of data, the method of least squares, and state/velocity estimators Flight simulators, covering the motion of an aircraft, the equations of motion, short period pitching motion, and phugoid motion Established engineers and programmers, along with students and academics in related programs of study, can harness the comprehensive information in Computer Models of Process Dynamics to gain mastery over the subject and be ready to use their knowledge in many practical applications in the field.
Data Engineering with Google Cloud Platform
Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineerKey Features: Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solutionLearn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelinesDiscover tips to prepare for and pass the Professional Data Engineer examBook Description: With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards.Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP.By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What You Will Learn: Load data into BigQuery and materialize its output for downstream consumptionBuild data pipeline orchestration using Cloud ComposerDevelop Airflow jobs to orchestrate and automate a data warehouseBuild a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc clusterLeverage Pub/Sub for messaging and ingestion for event-driven systemsUse Dataflow to perform ETL on streaming dataUnlock the power of your data with Data StudioCalculate the GCP cost estimation for your end-to-end data solutionsWho this book is for: This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.
Exploring Big Historical Data: The Historian’s Macroscope (Second Edition)
Every day, more and more kinds of historical data become available, opening exciting new avenues of inquiry but also new challenges. This updated and expanded book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Originally authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.
Exploring Big Historical Data: The Historian’s Macroscope (Second Edition)
Every day, more and more kinds of historical data become available, opening exciting new avenues of inquiry but also new challenges. This updated and expanded book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Originally authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.
Frontiers of Algorithmics
This book constitutes the proceedings of the 15th International Workshop on Frontiers in Algorithmics, FAW 2021, held in conjunction with second International Joint Conference on Theoretical Computer Science (IJTCS 2021), as IJTCS-FAW 2021, in Beijing, China, in August 2021. The conference IJTCS-FAW 2021 was held in hybrid mode due to the COVID-19 pandemic. The 5 full papers presented in this volume were carefully reviewed and selected from 9 submissions. The joint conference provides a focused forum on Algorithmic Game Theory, Blockchain, Multi-agent Reinforcement Learning, Quantum Computation, Theory of Machine Learning, Machine Learning, Formal Method, Algorithm and Complexity, and EconCS.
Internet of Things. Technology and Applications
This book constitutes the refereed post-conference proceedings of the Fourth IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2021, held virtually in November 2021. The 15 full papers presented were carefully reviewed and selected from 33 submissions. Also included is a summary of two panel sessions held at the conference. The papers are organized in the following topical sections: challenges in IoT Applications and Research, Modernizing Agricultural Practice Using IoT, Cyber-physical IoT systems in Wildfire Context, IoT for Smart Health, Security, Methods.
Database Dreaming Volume I
Along with its companion volume (Database Dreaming Volume II), this book offers a collection of essays on the general topic of relational databases and relational database technology. Most of those essays, though not all, have been published before, but only in journals and magazines that are now hard to find or in books that are now out of print. Here's a lightly edited excerpt from the preface (so this is the author speaking): I went back and reviewed all of those early essays, looking for ones that seemed worth reviving (or, rather, revising and reviving) at this time. Of course, some of them definitely weren't! However, out of a total of around 130 original papers, I did find some 20 or so that seemed to me worth preserving and hadn't already been incorporated in, or superseded by, more recent books of mine. So I tracked down the original versions of those 20 or so papers and set to work. When I was done, though, I found I had somewhere in excess of 600 pages on my hands-too much, in my view, for just one book, and so I split them across two separate volumes. Highlights of the present volume include a discussion of the difficulties involved in providing a relational interface to a nonrelational system; a tutorial on the quantifiers and what happens to them under three-valued logic; an examination of the effect of user defined types on optimization; some thoughts on normalization and database design tools; and caveats regarding certain important database operators, especially outer join and negation.
Database Dreaming Volume II
Along with its companion volume (Database Dreaming Volume I), this book offers a collection of essays on the general topic of relational databases and relational database technology. Most of those essays, though not all, have been published before, but only in journals and magazines that are now hard to find or in books that are now out of print. Here's a lightly edited excerpt from the preface (so this is the author speaking): I went back and reviewed all of those early essays, looking for ones that seemed worth reviving (or, rather, revising and reviving) at this time. Of course, some of them definitely weren't! However, out of a total of around 130 original papers, I did find some 20 or so that seemed to me worth preserving and hadn't already been incorporated in, or superseded by, more recent books of mine. So I tracked down the original versions of those 20 or so papers and set to work. When I was done, though, I found I had somewhere in excess of 600 pages on my hands-too much, in my view, for just one book, and so I split them across two separate volumes. Highlights of the present volume include a detailed explanation of the multiple assignment operator and why it's so essential; an investigation into why object and database technologies are so much more different than they're often made out to be; a critical examination of SQL's support for pointers ("references"); a tutorial on the counterintuitive (but crucial) concept of tables with no columns; and an annotated and extended debate between the author and E. F. Codd, inventor of the relational model, on the subject of nulls and three-valued logic.
Getting Started with Elastic Stack 8.0
Use the Elastic Stack for search, security, and observability-related use cases while working with large amounts of data on-premise and on the cloudKey Features: Learn the core components of the Elastic Stack and how they work togetherBuild search experiences, monitor and observe your environments, and defend your organization from cyber attacksGet to grips with common architecture patterns and best practices for successfully deploying the Elastic StackBook Description: The Elastic Stack helps you work with massive volumes of data to power use cases in the search, observability, and security solution areas.This three-part book starts with an introduction to the Elastic Stack with high-level commentary on the solutions the stack can be leveraged for. The second section focuses on each core component, giving you a detailed understanding of the component and the role it plays. You'll start by working with Elasticsearch to ingest, search, analyze, and store data for your use cases. Next, you'll look at Logstash, Beats, and Elastic Agent as components that can collect, transform, and load data. Later chapters help you use Kibana as an interface to consume Elastic solutions and interact with data on Elasticsearch. The last section explores the three main use cases offered on top of the Elastic Stack. You'll start with a full-text search and look at real-world outcomes powered by search capabilities. Furthermore, you'll learn how the stack can be used to monitor and observe large and complex IT environments. Finally, you'll understand how to detect, prevent, and respond to security threats across your environment. The book ends by highlighting architecture best practices for successful Elastic Stack deployments.By the end of this book, you'll be able to implement the Elastic Stack and derive value from it.What You Will Learn: Configure Elasticsearch clusters with different node types for various architecture patternsIngest different data sources into Elasticsearch using Logstash, Beats, and Elastic AgentBuild use cases on Kibana including data visualizations, dashboards, machine learning jobs, and alertsDesign powerful search experiences on top of your data using the Elastic StackSecure your organization and learn how the Elastic SIEM and Endpoint Security capabilities can helpExplore common architectural considerations for accommodating more complex requirementsWho this book is for: Developers and solutions architects looking to get hands-on experience with search, security, and observability-related use cases on the Elastic Stack will find this book useful. This book will also help tech leads and product owners looking to understand the value and outcomes they can derive for their organizations using Elastic technology. No prior knowledge of the Elastic Stack is required.
Data Lakehouse in Action
Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patternsKey Features: Understand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description: The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success.The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application.By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner.What You Will Learn: Understand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for: This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required.
Azure Data Engineer Associate Certification Guide
Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certificationKey Features: Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification examExplore the various Azure services for building end-to-end data solutionsGain a solid understanding of building secure and sustainable data solutions using Azure servicesBook Description: Azure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other.Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you'll work on sample questions and answers to familiarize yourself with the pattern of the exam.By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What You Will Learn: Gain intermediate-level knowledge of Azure the data infrastructureDesign and implement data lake solutions with batch and stream pipelinesIdentify the partition strategies available in Azure storage technologiesImplement different table geometries in Azure Synapse AnalyticsUse the transformations available in T-SQL, Spark, and Azure Data FactoryUse Azure Databricks or Synapse Spark to process data using NotebooksDesign security using RBAC, ACL, encryption, data masking, and moreMonitor and optimize data pipelines with debugging tipsWho this book is for: This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack.The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book.
Beginning Relational Data Modeling
Data storage design, and awareness of how data needs to be utilized within an organization, is of prime importance in ensuring that company data systems work efficiently. Beginning Relational Data Modeling will lead you step by step through the process of developing an effective logical data model for your relational database model.
Exploring Graphs with Elixir
Data is everywhere - it's just not very well connected, which makes it super hard to relate dataset to dataset. Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data. Add Elixir, with its awesome power of concurrency, and you'll soon be mastering data networks. Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures. We'll start from the basics and examine the main graph paradigms. Get ready to embrace the world of connected data! Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items. These data networks, or graphs in math speak, are typically stored and queried using graph databases. Elixir, with its noted support for fault tolerance and concurrency, stands out as a language eminently suited to processing sparsely connected and distributed datasets. Using Elixir and graph-aware packages in the Elixir ecosystem, you'll easily be able to fit your data to graphs and networks, and gain new information insights. Build a testbed app for comparing native graph data with external graph databases. Develop a set of applications under a single umbrella app to drill down into graph structures. Build graph models in Elixir, and query graph databases of various stripes - using Cypher and Gremlin with property graphs and SPARQL with RDF graphs. Transform data from one graph modeling regime to another. Understand why property graphs are especially good at graph traversal problems, while RDF graphs shine at integrating different semantic models and can scale up to web proportions. Harness the outstanding power of concurrent processing in Elixir to work with distributed graph datasets and manage data at scale. What You Need: To follow along with the book, you should have Elixir 1.10+ installed. The book will guide you through setting up an umbrella application for a graph testbed using a variety of graph databases for which Java SDK 8+ is generally required. Instructions for installing the graph databases are given in an appendix.
Mobile Networks and Management
This book constitutes the refereed post-conference proceedings of the 11th International Conference on Mobile Networks and Management, MONAMI 2021, held in October 2021. The conference was held virtually due to the COVID-19 pandemic. The 26 full papers were carefully reviewed and selected from 53 submissions. The papers are divided into groups of content as follows: The application of artificial intelligence for smart city; Advanced technology in edge and fog computing; Emerging technologies and applications in mobile networks and management; and Recent advances in communications and computing.
Fundamentals of Logic and Computation
This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. The book combines theoretical teaching and practical exercises; the latter is realised in Isabelle/HOL, a modern theorem prover, and PAT, an industry-scale model checker. I also give entry-level tutorials on the two software to help the reader get started. By the end of the book, the reader should be proficient in both software. Content-wise, this book focuses on the syntax, semantics and proof theory of various logics; automata theory, formal languages, computability and complexity. The final chapter closes the gap with a discussion on the insight that links logic with computation. This book is written for a high-level undergraduate course or a Master's course. The hybrid skill set of practical theorem proving and model checking should be helpful for the future of readers should they pursue a research career or engineering informal methods.
The Trouble with Big Data
This book is available as open access through the Bloomsbury Open programme and is available on www.bloomsburycollections.com. It is funded by Trinity College Dublin, DARIAH-EU and the European Commission.This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.
Extreme DAX
Discover the true power of DAX and build advanced DAX solutions for practical business scenariosKey Features: Solve complex business problems within Microsoft BI tools including Power BI, SQL Server, and ExcelDevelop a conceptual understanding of critical business data modeling principlesLearn the subtleties of Power BI data visualizations, evaluation context, context transition, and filteringBook Description: This book helps business analysts generate powerful and sophisticated analyses from their data using DAX and get the most out of Microsoft Business Intelligence tools.Extreme DAX will first teach you the principles of business intelligence, good model design, and how DAX fits into it all. Then, you'll launch into detailed examples of DAX in real-world business scenarios such as inventory calculations, forecasting, intercompany business, and data security. At each step, senior DAX experts will walk you through the subtleties involved in working with Power BI models and common mistakes to look out for as you build advanced data aggregations.You'll deepen your understanding of DAX functions, filters, and measures, and how and when they can be used to derive effective insights. You'll also be provided with PBIX files for each chapter, so that you can follow along and explore in your own time.What You Will Learn: Understand data modeling concepts and structures before you start working with DAXGrasp how relationships in Power BI models are different from those in RDBMSesSecure aggregation levels, attributes, and hierarchies using PATH functions and row-level securityGet to grips with the crucial concept of contextApply advanced context and filtering functions including TREATAS, GENERATE, and SUMMARIZEExplore dynamically changing visualizations with helper tables and dynamic labels and axesWork with week-based calendars and understand standard time-intelligenceEvaluate investments intelligently with the XNPV and XIRR financial DAX functionsWho this book is for: Extreme DAX is written for analysts with a working knowledge of DAX in Power BI or other Microsoft analytics tools. It will help you upgrade your knowledge and work with analytical models more effectively, so you'll need practical experience with DAX before you can get started.
The Hundred-Page Machine Learning Book
Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics - both theory and practice - that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."Aur矇lien G矇ron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "I would highly recommend "The Hundred-Page Machine Learning Book" for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."
Supercomputing
This book constitutes the refereed post-conference proceedings of the 7th Russian Supercomputing Days, RuSCDays 2021, held in Moscow, Russia, in September 2021.The 37 revised full papers and 3 short papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: supercomputer simulation; HPC, BigData, AI: architectures, technologies, tools; and distributed and cloud computing.
Logic Programming Languages
This collection of current research on logic programming languages presents results from a three-year, ESPRIT-funded effort to explore the integration of the foundational issues of functional, logic, and object-oriented programming. It offers valuable insights into the fast-developing extensions of logic programming with functions, constraints, concurrency, and objects. Chapters are grouped according to the unifying themes of functional programming, constraint, logic programming, and object-oriented programming.
Communication Technologies for Vehicles
This book constitutes the refereed proceedings of the 16th International Workshop on Communication Technologies for Vehicles, Nets4Cars/Nets4Trains/Nets4Aircraft 2021, held in Madrid, Spain, in November 2021. The 6 full and 2 short papers were carefully reviewed and selected from numerous submissions. The selected papers present original research results in areas related to the physical layer, communication protocols and standards, mobility and traffic models, experimental and field operational testing, and performance analysis
Algorithmic Aspects in Information and Management
This book constitutes the proceedings of the 15th International Conference on Algorithmic Aspects in Information and Management, AAIM 2021, which was held online during December 20-22, 2021. The conference was originally planned to take place in Dallas, Texas, USA, but changed to a virtual event due to the COVID-19 pandemic. The 38 regular papers included in this book were carefully reviewed and selected from 62 submissions. They were organized in the following topical sections: approximation algorithms; scheduling; nonlinear combinatorial optimization; network problems; blockchain, logic, complexity and reliability; and miscellaneous.
Building Big Data Pipelines with Apache Beam
Implement, run, operate, and test data processing pipelines using Apache BeamKey Features: Understand how to improve usability and productivity when implementing Beam pipelinesLearn how to use stateful processing to implement complex use cases using Apache BeamImplement, test, and run Apache Beam pipelines with the help of expert tips and techniquesBook Description: Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing.This book will help you to confidently build data processing pipelines with Apache Beam. You'll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You'll also learn how to test and run the pipelines efficiently. As you progress, you'll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you'll understand advanced Apache Beam concepts, such as implementing your own I/O connectors.By the end of this book, you'll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.What You Will Learn: Understand the core concepts and architecture of Apache BeamImplement stateless and stateful data processing pipelinesUse state and timers for processing real-time event processingStructure your code for reusabilityUse streaming SQL to process real-time data for increasing productivity and data accessibilityRun a pipeline using a portable runner and implement data processing using the Apache Beam Python SDKImplement Apache Beam I/O connectors using the Splittable DoFn APIWho this book is for: This book is for data engineers, data scientists, and data analysts who want to learn how Apache Beam works. Intermediate-level knowledge of the Java programming language is assumed.
Power Pivot and Power Bi
Microsoft PowerPivot is a free add-on to Excel from Microsoft that allows users to produce new kinds of reports and analyses that were simply impossible before, and this book is the first to tackle DAX formulas, the core capability of PowerPivot, from the perspective of the Excel audience. Written by the world's foremost PowerPivot blogger and practitioner, the book's concepts and approach are introduced in a step-by-step manner tailored to the learning style of Excel users everywhere. The techniques presented allow users to produce, in hours or even minutes, results that formerly would have taken entire teams weeks or months to produce. The "pattern-like" techniques and best practices contained in this book have been developed and refined over two years of onsite training with Excel users around the world, and the key lessons from those seminars costing thousands of dollars per day are now available within the pages of this easy-to-follow guide. This updated edition covers new features introduced with Office 2015.
Data Engineering with AWS
The missing expert-led manual for the AWS ecosystem - go from foundations to building data engineering pipelines effortlesslyPurchase of the print or Kindle book includes a free eBook in the PDF format.Key Features: Learn about common data architectures and modern approaches to generating value from big dataExplore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelinesLearn how to architect and implement data lakes and data lakehouses for big data analytics Book Description: Knowing how to architect and implement complex data pipelines is a highly sought-after skill. Data engineers are responsible for building these pipelines that ingest, transform, and join raw datasets - creating new value from the data in the process.Amazon Web Services (AWS) offers a range of tools to simplify a data engineer's job, making it the preferred platform for performing data engineering tasks.This book will take you through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. The book also teaches you about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data.By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What You Will Learn: Understand data engineering concepts and emerging technologiesIngest streaming data with Amazon Kinesis Data FirehoseOptimize, denormalize, and join datasets with AWS Glue StudioUse Amazon S3 events to trigger a Lambda process to transform a fileRun complex SQL queries on data lake data using Amazon AthenaLoad data into a Redshift data warehouse and run queriesCreate a visualization of your data using Amazon QuickSightExtract sentiment data from a dataset using Amazon ComprehendWho this book is for: This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone who is new to data engineering and wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful.A basic understanding of big data-related topics and Python coding will help you get the most out of this book but is not needed. Familiarity with the AWS console and core services is also useful but not necessary.
