Hands-On SQL Server 2019 Analysis Services
Get up to speed with the new features added to Microsoft SQL Server 2019 Analysis Services and create models to support your businessKey FeaturesExplore tips and tricks to design, develop, and optimize end-to-end data analytics solutions using Microsoft's technologiesLearn tabular modeling and multi-dimensional cube design development using real-world examplesImplement Analysis Services to help you make productive business decisionsBook DescriptionSQL Server Analysis Services (SSAS) continues to be a leading enterprise-scale toolset, enabling customers to deliver data and analytics across large datasets with great performance. This book will help you understand MS SQL Server 2019's new features and improvements, especially when it comes to SSAS.First, you'll cover a quick overview of SQL Server 2019, learn how to choose the right analytical model to use, and understand their key differences. You'll then explore how to create a multi-dimensional model with SSAS and expand on that model with MDX. Next, you'll create and deploy a tabular model using Microsoft Visual Studio and Management Studio. You'll learn when and how to use both tabular and multi-dimensional model types, how to deploy and configure your servers to support them, and design principles that are relevant to each model. The book comes packed with tips and tricks to build measures, optimize your design, and interact with models using Excel and Power BI. All this will help you visualize data to gain useful insights and make better decisions. Finally, you'll discover practices and tools for securing and maintaining your models once they are deployed.By the end of this MS SQL Server book, you'll be able to choose the right model and build and deploy it to support the analytical needs of your business.What you will learnDetermine the best analytical model using SSASCover the core aspects involved in MDX, including writing your first queryImplement calculated tables and calculation groups (new in version 2019) in DAXCreate and deploy tabular and multi-dimensional models on SQL 2019Connect and create data visualizations using Excel and Power BIImplement row-level and other data security methods with tabular and multi-dimensional modelsExplore essential concepts and techniques to scale, manage, and optimize your SSAS solutionsWho this book is forThis Microsoft SQL Server book is for BI professionals and data analysts who are looking for a practical guide to creating and maintaining tabular and multi-dimensional models using SQL Server 2019 Analysis Services. A basic working knowledge of BI solutions such as Power BI and database querying is required.
Guide to Business Data Analytics
The Guide to Business Data Analytics provides a foundational understanding of business data analytics concepts and includes how to develop a framework; key techniques and application; how to identify, communicate and integrate results; and more. This guide acts as a reference for the practice of business data analytics and is a companion resource for the Certification in Business Data Analytics (IIBA(R)- CBDA).Explore more information about the Certification in Business Data Analytics at IIBA.org/CBDA.About International Institute of Business AnalysisInternational Institute of Business Analysis(TM) (IIBA(R)) is a professional association dedicated to supporting business analysis professionals deliver better business outcomes. IIBA connects almost 30,000 Members, over 100 Chapters, and more than 500 training, academic, and corporate partners around the world. As the global voice of the business analysis community, IIBA supports recognition of the profession, networking and community engagement, standards and resource development, and comprehensive certification programs.IIBA Publications IIBA publications offer a wide variety of knowledge and insights into the profession and practice of business analysis for the entire business community. Standards such as A Guide to the Business Analysis Body of Knowledge(R) (BABOK(R) Guide), the Agile Extension to the BABOK(R) Guide, and the Global Business Analysis Core Standard represent the most commonly accepted practices of business analysis around the globe. IIBA's reports, research, whitepapers, and studies provide guidance and best practices information to address the practice of business analysis beyond the global standards and explore new and evolving areas of practice to deliver better business outcomes. Learn more at iiba.org.
Oracle Database Transactions and Locking Revealed
Access much-needed information for building scalable, high-concurrency applications and deploying them against the Oracle Database. This new edition is updated to be current with Oracle Database 19. It includes a new chapter with troubleshooting recipes to help you quickly diagnose and resolve locking problems that are urgent and block production.Good transaction design is an important facet of highly-concurrent applications that are run by hundreds, even thousands, of users who are executing transactions at the same time. Transaction design, in turn, relies on a good understanding of how the database engine manages the locking of resources to prevent access conflicts and data loss that might otherwise result from concurrent access to data in the database. This book provides a solid and accurate explanation of how locking and concurrency are dealt with by Oracle Database. You will learn how the Oracle Database architecture accommodates user transactions, and how you can write code to mesh with the way in which Oracle Database is designed to operate.Oracle Database Transactions and Locking Revealed covers in detail the various lock types, and also different locking schemes such as pessimistic and optimistic locking. Then you will learn about transaction isolation and multi-version concurrency, and how the various lock types support Oracle Database's transactional features. You will learn tips for transaction design, as well as some bad practices and habits to avoid. Coverage is also given to redo and undo, and their role in concurrency. The book is loaded with insightful code examples that drive home each concept. This is an important book that anyone developing highly-concurrent applications will want to have handy on their shelf. What You Will LearnAvoid application lockups due to conflicts over accessing the same resourceUnderstand how Oracle prevents one application from overwriting another's modificationsCreate transaction designs that mesh with how Oracle Database is designedBuild high-throughput applications supporting thousands of concurrent usersDesign applications to take full advantage of Oracle's powerful database engineGain a fundamental knowledge of Oracle's transaction and locking architectureDevelop techniques to quickly diagnose and resolve common locking issues Who This Book Is ForOracle developers and database administrators faced with troubleshooting and solving deadlocks, locking contention, and similar problems that are encountered in high-concurrency environments; and application developers wanting to design their applications to excel at multi-user concurrency by taking full advantage of Oracle Database's multi-versioning and concurrency support
Projection-Based Clustering Through Self-Organization and Swarm Intelligence
This book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Big Data Analytics and Artificial Intelligence Against Covid-19: Innovation Vision and Approach
This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19.
MySQL & JSON A Practical Programming Guide
JSON - JavaScript Object Notation - has become the data interchange format for most developers. MySQL added a JSON data type plus NoSQL JSON Document Store so developers can work with both relational tables and schema-less JSON document collections in version 5.7. MySQL 8.0 has introduced many new JSON centric features. This book is a practical guide on how to take advantage of JSON data with a MySQL database with numerous easy-to-follow examples, clear explanations, and coding examples. This book will become your reference guide on using JSON data with a MySQL database, the numerous functions used with JSON data, the functions that can convert non-JSON relational data into JSON, the MySQL Document Store, and converting JSON data temporarily into structured (SQL) data for processing with SQL commands.
Fundamentals of Quantum Programming in Ibm’s Quantum Computers
This textbook introduces major topics that include quantum bits, superposition, entanglement, logic gates, quantum search algorithm, quantum Fourier transform, inverse quantum Fourier transform, Shor's order-finding algorithm and phase estimation. Everyone can write algorithms and programs in the cloud making using IBM's quantum computers that support IBM Q Experience which contains the composer, open quantum assembly language, simulators and real quantum devices. Furthermore, this book teaches you how to use open quantum assembly language to write quantum programs for dealing with complex problems. Through numerous examples and exercises, readers will learn how to write a quantum program with open quantum assembly language for solving any problem from start to complete. This book includes six main chapters: -Quantum Bits and Quantum Gates-learn what quantum bits are, how to declare and measure them, what quantum gates are and how they work on a simulator or a real device in the cloud. -Boolean Algebra and its Applications-learn how to decompose CCNOT gate into six CNOT gates and nine gates of one bit and how to use NOT gates, CNOT gates and CCNOT gates to implement logic operations including NOT, OR, AND, NOR, NAND, Exclusive-OR (XOR) and Exclusive-NOR (XNOR). -Quantum Search Algorithm and its Applications-learn core concepts of quantum search algorithm and how to write quantum programs to implement core concepts of quantum search algorithm for solving two famous NP-complete problems that are the satisfiability problem in n Boolean variables and m clauses and the clique problem in a graph with n vertices and q edges. -Quantum Fourier Transform and its Applications-learn core concepts of quantum Fourier transform and inverse quantum Fourier transform and how to write quantum programs to implement them for solving two real applications that are to compute the period and the frequency of two given oracular functions. -Order-Finding and Factoring-learn core concepts of Shor's order-finding algorithm and how to write quantum programs to implement Shor's order-finding algorithm for completing the prime factorization to 15. Phase Estimation and its Applications-learn core concepts of phase estimation and quantum counting and how to write quantum programs to implement them to compute the number of solution(s) in the independent set problem in a graph with two vertices and one edge.
Database Systems for Advanced Applications
The 4 volume set LNCS 12112-12114 constitutes the papers of the 25th International Conference on Database Systems for Advanced Applications which will be held online in September 2020. The 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions. The conference program presents the state-of-the-art R&D activities in database systems and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry.
Practical Optimization
This textbook provides a hands-on treatment of the subject of optimization. A comprehensive set of problems and exercises makes it suitable for use in one or two semesters of an advanced undergraduate course or a first-year graduate course. Each half of the book contains a full semester's worth of complementary yet stand-alone material. The practical orientation of the topics chosen and a wealth of useful examples also make the book suitable as a reference work for practitioners in the field. In this second edition the authors have added sections on recent innovations, techniques, and methodologies.
KI & Recht Kompakt
Das Buch gibt einen kompakten Einblick in alle wesentlichen Rechtsfragen rund um den Einsatz K羹nstlicher Intelligenz in Unternehmen oder Produkten. Versierte Autoren mit Praxiserfahrung erl瓣utern die wichtigsten rechtlichen Themen beim Einsatz intelligenter Systeme und behandeln nach einer Einf羹hrung in die technischen Grundlagen die Auswirkungen und Besonderheiten K羹nstlicher Intelligenz in den Bereichen: ZivilrechtVertragsgestaltungLizenzierung HaftungImmaterialg羹terrechte DatenschutzStrafrechtArbeitsrecht
Learn Blockchain by Building One
As cryptocurrencies and their underlying data structure, blockchains, become further intertwined in our daily lives, a full understanding of them is essential to anyone who wants to keep up and remain informed of the future of finance. There is no better learning method than a hands-on one, and Learn Blockchain by Building One offers just that.Develop your own blockchain using Python with step-by-step instructions from author Daniel van Flymen, an expert in the field. You will come away with a confident working knowledge of popular cryptocurrencies such as Bitcoin and Ethereum and which foundations make them work. Through helpful exercises and real-world examples, you will understand the core concepts of peer-to-peer networking, proof of work, hashing, encryption, and digital signatures.Learn Blockchain by Building One gives you timely, real-world lessons in blockchain and cryptocurrencies that you will need as our modern society becomes increasingly digitally sophisticated. The lasting implications of such technology, including the security of personal transactions and the role of government regulation, are not to be underestimated. Stay ahead of the curve and become a confident blockchain builder now!What You Will LearnDevelop a fully-fledged blockchain in PythonObtain a ground-up understanding of of Proof of WorkGrasp core cryptographic concepts, such as hashing, encryption, and digital signaturesUnderstand how gossip protocols and peer-to-peer networking works by implementing a TCP client-serverRealize the differences and trade-offs between popular blockchains such as Bitcoin and Ethereum Who This Book Is For This book is aimed at intermediate programmers in any area from finance to academia. Readers should be comfortable reading and writing basic Python.
Database and Expert Systems Applications
The double volumes LNCS 12391-12392 constitutes the papers of the 31st International Conference on Database and Expert Systems Applications, DEXA 2020, which will be held online in September 2020. The 38 full papers presented together with 20 short papers plus 1 keynote papers in these volumes were carefully reviewed and selected from a total of 190 submissions.
Databases and Information Systems
This book constitutes the refereed proceedings of the 14th International Baltic Conference on Databases and Information Systems, DB&IS 2020, held in Tallinn, Estonia, in June 2020.*The 22 revised papers presented were carefully reviewed and selected from 52 submissions. The papers are centered around topics like architectures and quality of information systems, artificial intelligence in information systems, data and knowledge engineering, enterprise and information systems engineering, security of information systems. *The conference was held virtully due to the COVID-19 pandemic.
Knowledge Graphs and Big Data Processing
Foundations.- Chapter 1. Ecosystem of Big Data.- Chapter 2. Knowledge Graphs: The Layered Perspective.- Chapter 3. Big Data Outlook, Tools, and Architectures.- Architecture.- Chapter 4. Creation of Knowledge Graphs.- Chapter 5. Federated Query Processing.- Chapter 6. Reasoning in Knowledge Graphs: An Embeddings Spotlight.- Methods and Solutions.- Chapter 7. Scalable Knowledge Graph Processing using SANSA.- Chapter 8. Context-Based Entity Matching for Big Data.- Applications.- Chapter 9. Survey on Big Data Applications.- Chapter 10. Case Study from the Energy Domain.
Multimedia Technology and Enhanced Learning
This two-volume book constitutes the refereed proceedings of the Second International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2020, held in Leicester, United Kingdom, in April 2020. Due to the COVID-19 pandemic all papers were presented in YouTubeLive. The 83 revised full papers have been selected from 158 submissions. They describe new learning technologies which range from smart school, smart class and smart learning at home and which have been developed from new technologies such as machine learning, multimedia and Internet of Things.
Advances in Databases and Information Systems24th European Conference, Adbis 2020, Lyon, F
This book constitutes the proceedings of the 24th European Conference on Advances in Databases and Information Systems, ADBIS 2020, held in Lyon, France, in August 2020.* The 13 full papers presented were carefully reviewed and selected from 82 submissions. The papers cover a wide range of topics from different areas of research in database and information systems technologies and their advanced applications from theoretical foundations to optimizing index structures. They are organized in the following topical sections: keynote extended abstracts; data access and database performance; machine learning; semantic web; data processing; and data analytics. * The conference was held virtually due to the COVID-19 pandemic.
Data Management Technologies and Applications
This book constitutes the thoroughly refereed proceedings of the 8th International Conference on Data Management Technologies and Applications, DATA 2019, held in Prague, Czech Republic, in July 2019. The 8 revised full papers were carefully reviewed and selected from 90 submissions. The papers deal with the following topics: decision support systems, data analytics, data and information quality, digital rights management, big data, knowledge management, ontology engineering, digital libraries, mobile databases, object-oriented database systems, and data integrity.
Transactions on Large-Scale Data- And Knowledge-Centered Systems XLV
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 45th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight revised selected regular papers. Topics covered include data analysis, information extraction, blockchains, and big data.
Metabase Up and Running
Ask questions of your data and gain insights to make better business decisions using the open source business intelligence tool, MetabaseKey FeaturesDeploy Metabase applications to let users across your organization interact with itLearn to create data visualizations, charts, reports, and dashboards with the help of a variety of examplesUnderstand how to embed Metabase into your website and send out reports automatically using email and SlackBook DescriptionMetabase is an open source business intelligence tool that helps you use data to answer questions about your business. This book will give you a detailed introduction to using Metabase in your organization to get the most value from your data.You'll start by installing and setting up Metabase on your local computer. You'll then progress to handling the administration aspect of Metabase by learning how to configure and deploy Metabase, manage accounts, and execute administrative tasks such as adding users and creating permissions and metadata. Complete with examples and detailed instructions, this book shows you how to create different visualizations, charts, and dashboards to gain insights from your data. As you advance, you'll learn how to share the results with peers in your organization and cover production-related aspects such as embedding Metabase and auditing performance. Throughout the book, you'll explore the entire data analytics process-from connecting your data sources, visualizing data, and creating dashboards through to daily reporting.By the end of this book, you'll be ready to implement Metabase as an integral tool in your organization.What you will learnExplore different types of databases and find out how to connect them to MetabaseDeploy and host Metabase securely using Amazon Web ServicesUse Metabase's user interface to filter and aggregate data on single and multiple tablesBecome a Metabase admin by learning how to add users and create permissionsAnswer critical questions for your organization by using the Notebook editor and writing SQL queriesUse the search functionality to search through tables, dashboards, and metricsWho this book is forThis book is for business analysts, data analysts, data scientists, and other professionals who want to become well-versed with business intelligence and analytics using Metabase. This book will also appeal to anyone who wants to understand their data to extract meaningful insights with the help of practical examples. A basic understanding of data handling and processing is necessary to get started with this book.
ETL with Azure Cookbook
Explore the latest Azure ETL techniques both on-premises and in the cloud using Azure services such as SQL Server Integration Services (SSIS), Azure Data Factory, and Azure DatabricksKey FeaturesUnderstand the key components of an ETL solution using Azure Integration ServicesDiscover the common and not-so-common challenges faced while creating modern and scalable ETL solutionsProgram and extend your packages to develop efficient data integration and data transformation solutionsBook DescriptionETL is one of the most common and tedious procedures for moving and processing data from one database to another. With the help of this book, you will be able to speed up the process by designing effective ETL solutions using the Azure services available for handling and transforming any data to suit your requirements.With this cookbook, you'll become well versed in all the features of SQL Server Integration Services (SSIS) to perform data migration and ETL tasks that integrate with Azure. You'll learn how to transform data in Azure and understand how legacy systems perform ETL on-premises using SSIS. Later chapters will get you up to speed with connecting and retrieving data from SQL Server 2019 Big Data Clusters, and even show you how to extend and customize the SSIS toolbox using custom-developed tasks and transforms. This ETL book also contains practical recipes for moving and transforming data with Azure services, such as Data Factory and Azure Databricks, and lets you explore various options for migrating SSIS packages to Azure. Toward the end, you'll find out how to profile data in the cloud and automate service creation with Business Intelligence Markup Language (BIML).By the end of this book, you'll have developed the skills you need to create and automate ETL solutions on-premises as well as in Azure.What you will learnExplore ETL and how it is different from ELTMove and transform various data sources with Azure ETL and ELT servicesUse SSIS 2019 with Azure HDInsight clustersDiscover how to query SQL Server 2019 Big Data Clusters hosted in AzureMigrate SSIS solutions to Azure and solve key challenges associated with itUnderstand why data profiling is crucial and how to implement it in Azure DatabricksGet to grips with BIML and learn how it applies to SSIS and Azure Data Factory solutionsWho this book is forThis book is for data warehouse architects, ETL developers, or anyone who wants to build scalable ETL applications in Azure. Those looking to extend their existing on-premise ETL applications to use big data and a variety of Azure services or others interested in migrating existing on-premise solutions to the Azure cloud platform will also find the book useful. Familiarity with SQL Server services is necessary to get the most out of this book.
Learn Quantum Computing with Python and IBM Quantum Experience
A step-by-step guide to learning the implementation and associated methodologies in quantum computing with the help of the IBM Quantum Experience, Qiskit, and Python that will have you up and running and productive in no timeKey featuresDetermine the difference between classical computers and quantum computersUnderstand the quantum computational principles such as superposition and entanglement and how they are leveraged on IBM Quantum Experience systemsRun your own quantum experiments and applications by integrating with QiskitBook DescriptionIBM Quantum Experience is a platform that enables developers to learn the basics of quantum computing by allowing them to run experiments on a quantum computing simulator and a real quantum computer. This book will explain the basic principles of quantum mechanics, the principles involved in quantum computing, and the implementation of quantum algorithms and experiments on IBM's quantum processors.You will start working with simple programs that illustrate quantum computing principles and slowly work your way up to more complex programs and algorithms that leverage quantum computing. As you build on your knowledge, you'll understand the functionality of IBM Quantum Experience and the various resources it offers. Furthermore, you'll not only learn the differences between the various quantum computers but also the various simulators available. Later, you'll explore the basics of quantum computing, quantum volume, and a few basic algorithms, all while optimally using the resources available on IBM Quantum Experience.By the end of this book, you'll learn how to build quantum programs on your own and have gained practical quantum computing skills that you can apply to your business.What you will learnExplore quantum computational principles such as superposition and quantum entanglementBecome familiar with the contents and layout of the IBM Quantum ExperienceUnderstand quantum gates and how they operate on qubitsDiscover the quantum information science kit and its elements such as Terra and AerGet to grips with quantum algorithms such as Bell State, Deutsch-Jozsa, Grover's algorithm, and Shor's algorithmHow to create and visualize a quantum circuitWho this book is forThis book is for Python developers who are looking to learn quantum computing and put their knowledge to use in practical situations with the help of IBM Quantum Experience. Some background in computer science and high-school-level physics and math is required.
Big Data - Bigdata 2020
This book constitutes the proceedings of the 9th International Conference on Big Data, BigData 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 16 full and 3 short papers presented were carefully reviewed and selected from 52 submissions. The topics covered are Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions.
Nature-Inspired Computation in Data Mining and Machine Learning
Adaptive Improved Flower Pollination Algorithm for Global Optimization.- Algorithms for Optimization and Machine Learning over Cloud.- Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks.- Comparative analysis of different classifiers on crisis-related tweets: An elaborate study.- An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm.- Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services.
Database and Expert Systems Applications
This volume constitutes the refereed proceedings of the three workshops held at the 31st International Conference on Database and Expert Systems Applications, DEXA 2020, held in September 2020: The 11th International Workshop on Biological Knowledge Discovery from Data, BIOKDD 2020, the 4th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2020, the 2nd International Workshop on Machine Learning and Knowledge Graphs, MLKgraphs2019. Due to the COVID-19 pandemic the conference and workshops were held virtually. The 10 papers were thoroughly reviewed and selected from 15 submissions, and discuss a range of topics including: knowledge discovery, biological data, cyber security, cyber-physical system, machine learning, knowledge graphs, information retriever, data base, and artificial intelligence.
Real-Time Linked Dataspaces
1 Real-time Linked Dataspaces: A Data Platform for Intelligent Systems within Internet of Things-based Smart Environments.- 2 Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem.- 3 Dataspaces: Fundamentals, Principles, and Techniques.- 4 Fundamentals of Real-time Linked Dataspaces.- 5 Data Support Services for Real-time Linked Dataspaces.- 6 Catalog and Entity Management Service for Internet of Things-based Smart Environments.- 7 Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces.- 8 Enhancing the Discovery of Internet of Things-based Data Services in Real-time Linked Dataspaces.- 9 Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments.- 10 Stream and Event Processing Services for Real-time Linked Dataspaces.- 11 Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces.- 12 Dissemination of Internet of Things Streams in a Real-time Linked Dataspace.- 13 Approximate Semantic Event Processing in Real-time Linked Dataspaces.- 14 Enabling Intelligent Systems, Applications, and Analytics for Smart Environments using Real-time Linked Dataspaces.- 15 Autonomic Source Selection for Real-time Predictive Analytics using the Internet of Things and Open Data.- 16 Building Internet of Things-enabled Digital Twins and Intelligent Applications using a Real-time Linked Dataspace.- 17 A Model for Internet of Things Enhanced User Experience in Smart Environments.- 18 Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems.
MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition
Discrete Mathematics and Graph Theory
This textbook can serve as a comprehensive manual of discrete mathematics and graph theory for non-Computer Science majors; as a reference and study aid for professionals and researchers who have not taken any discrete math course before. It can also be used as a reference book for a course on Discrete Mathematics in Computer Science or Mathematics curricula. The study of discrete mathematics is one of the first courses on curricula in various disciplines such as Computer Science, Mathematics and Engineering education practices. Graphs are key data structures used to represent networks, chemical structures, games etc. and are increasingly used more in various applications such as bioinformatics and the Internet. Graph theory has gone through an unprecedented growth in the last few decades both in terms of theory and implementations; hence it deserves a thorough treatment which is not adequately found in any other contemporary books on discrete mathematics, whereas about 40% of this textbook is devoted to graph theory. The text follows an algorithmic approach for discrete mathematics and graph problems where applicable, to reinforce learning and to show how to implement the concepts in real-world applications.
Quantum Computing Solutions
Know how to use quantum computing solutions involving artificial intelligence (AI) algorithms and applications across different disciplines.Quantum solutions involve building quantum algorithms that improve computational tasks within quantum computing, AI, data science, and machine learning. As opposed to quantum computer innovation, quantum solutions offer automation, cost reduction, and other efficiencies to the problems they tackle.Starting with the basics, this book covers subsystems and properties as well as the information processing network before covering quantum simulators. Solutions such as the Traveling Salesman Problem, quantum cryptography, scheduling, and cybersecurity are discussed in step-by-step detail. The book presents code samples based on real-life problems in a variety of industries, such as risk assessment and fraud detection in banking. In pharma, you will look at drug discovery and protein-folding solutions. Supply chain optimizationand purchasing solutions are presented in the manufacturing domain. In the area of utilities, energy distribution and optimization problems and solutions are explained. Advertising scheduling and revenue optimization solutions are included from media and technology verticals. What You Will Learn Understand the mathematics behind quantum computingKnow the solution benefits, such as automation, cost reduction, and efficienciesBe familiar with the quantum subsystems and properties, including states, protocols, operations, and transformationsBe aware of the quantum classification algorithms: classifiers, and support and sparse support vector machinesUse AI algorithms, including probability, walks, search, deep learning, and parallelism Who This Book Is For Developers in Python and other languages interested inquantum solutions. The secondary audience includes IT professionals and academia in mathematics and physics. A tertiary audience is those in industry verticals such as manufacturing, banking, and pharma.
Guide to Intelligent Data ScienceHow to Intelligently Make Use of Real Data
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.This practical and systematic textbook/reference is a "need-to-have" tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a "need to use, need to keep" resource following one's exploration of thesubject.
Digital Forensic Education
In this book, the editors explain how students enrolled in two digital forensic courses at their institution are exposed to experiential learning opportunities, where the students acquire the knowledge and skills of the subject-matter while also learning how to adapt to the ever-changing digital forensic landscape. Their findings (e.g., forensic examination of different IoT devices) are also presented in the book. Digital forensics is a topic of increasing importance as our society becomes "smarter" with more of the "things" around us been internet- and inter-connected (e.g., Internet of Things (IoT) and smart home devices); thus, the increasing likelihood that we will need to acquire data from these things in a forensically sound manner. This book is of interest to both digital forensic educators and digital forensic practitioners, as well as students seeking to learn about digital forensics.
Digital Transformation of Collaboration
This proceedings is focused on the emerging concept of Collaborative Innovation Networks (COINs). COINs are at the core of collaborative knowledge networks, distributed communities taking advantage of the wide connectivity and the support of communication technologies, spanning beyond the organizational perimeter of companies on a global scale. The book presents the refereed conference papers from the 7th International Conference on COINs, October 8-9, 2019, in Warsaw, Poland. It includes papers for both application areas of COINs, (1) optimizing organizational creativity and performance, and (2) discovering and predicting new trends by identifying COINs on the Web through online social media analysis. Papers at COINs19 combine a wide range of interdisciplinary fields such as social network analysis, group dynamics, design and visualization, information systems and the psychology and sociality of collaboration, and intercultural analysis through the lens of online social media. They will cover most recent advances in areas from leadership and collaboration, trend prediction and data mining, to social competence and Internet communication.
Evolutionary Decision Trees in Large-Scale Data Mining
Evolutionary computation.- Decision trees in data mining.- Parallel and distributed computation.- Global induction of univariate trees.- Oblique and mixed decision trees.- Cost-sensitive tree induction.- Multi-test decision trees for gene expression data.- Parallel computations for evolutionary induction.
Concepts and Methods for a Librarian of the Web
The World Wide Web can be considered a huge library that in consequence needs a capable librarian responsible for the classification and retrieval of documents as well as the mediation between library resources and users. Based on this idea, the concept of the "Librarian of the Web" is introduced which comprises novel, librarian-inspired methods and technical solutions to decentrally search for text documents in the web using peer-to-peer technology.The concept's implementation in the form of an interactive peer-to-peer client, called "WebEngine", is elaborated on in detail. This software extends and interconnects common web servers creating a fully integrated, decentralised and self-organising web search system on top of the existing web structure. Thus, the web is turned into its own powerful search engine without the need for any central authority.This book is intended for researchers and practitioners having a solid background in the fields of Information Retrievaland Web Mining.
Advances in the Theory of Probabilistic and Fuzzy Data Scientific Methods with Applications
This book focuses on the advanced soft computational and probabilistic methods that the authors have published over the past few years. It describes theoretical results and applications, and discusses how various uncertainty measures - probability, plausibility and belief measures - can be treated in a unified way. It also examines approximations of four notable probability distributions (Weibull, exponential, logistic and normal) using a unified probability distribution function, and presents a fuzzy arithmetic-based time series model that provides an easy-to-use forecasting technique. Lastly, it proposes flexible fuzzy numbers for Likert scale-based evaluations. Featuring methods that can be successfully applied in a variety of areas, including engineering, economics, biology and the medical sciences, the book offers useful guidelines for practitioners and researchers.
Data Analysis in Bi-Partial Perspective: Clustering and Beyond
This book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representation of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis.The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard "academic" manner.
Smart Transportation Systems 2019
An Algorithm for Reducing Vehicles' Stop Behind the Bus Pre-Signals.- Estimation Models for the Safety Level of Indoor Space Pedestrian Flows.- Station-Level Hourly Bike Demand Prediction for Dynamic Repositioning in Bike Sharing Systems.- Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data.- Estimation Method of Saturation Flow Rate for Shared Left-Turn Lane at Signalized Intersection, PartⅠ Methodology.- Estimation Method of Saturation Flow Rate for Shared Left-Turn Lane at Signalized Intersection, PartⅡ Case Study.- Safety on Italian Highways, Impacts of the Highway Chauffeur System.- Restorable Robustness Considering Carbon Tax in Weekly Berth and Quay Crane Planning.
Complex Event Processing
Eine wichtige Aufgabe f羹r die IT der vernetzten Welt ist die maschinelle Auswertung und Verarbeitung von Informationen, die f羹r eine Anwendung relevant sind und 羹bers Netz verschickt werden. Mit Complex Event Processing (CEP) k繹nnen gro?e Mengen von zeitbehafteten Daten unterschiedlichster Art in nahezu Echtzeit analysiert und weiterverarbeitet werden. Die grundlegende Vorgehensweise beim CEP entspricht der menschlichen Entscheidungsfindung in Prozessabl瓣ufen des t瓣glichen Lebens und stellt eine Erweiterung bekannter Methoden des Data Analytics wie Data Mining, statistische Analyse oder regelbasierte Wissensverarbeitung dar. Typische Anwendungsgebiete sind Big-Data-Systeme, Internet of Things, Industrie 4.0.
97 Things about Ethics Everyone in Data Science Should Know
Most of the high-profile cases of real or perceived unethical activity in data science aren't matters of bad intent. Rather, they occur because the ethics simply aren't thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Concept: Jim Wilson How to Approach Ethical Transparency: Rado Kotorov Unbiased & Fair: Doug Hague Rules and Rational:: Christof Wolf Brenner The Truth About AI: Cassie Kozyrkov Cautionary Ethics Tales: Sherrill Hayes Fairness in the Age of Algorithms: Anna Jacobson The Ethical Data Storyteller: Brent Dykes Introducing Ethics: the Fully AI-Driven Cloud-Based Ethics Solution: Brian O'Neill Be Careful with Decisions of the Heart: Hugh Watson Understanding Passive Versus Proactive Ethics: Bill Schmarzo
Hands-On Graph Analytics with Neo4j
Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learningKey Features Get up and running with graph analytics with the help of real-world examples Explore various use cases such as fraud detection, graph-based search, and recommendation systems Get to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scaling Book Description Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You'll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You'll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You'll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you'll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you'll get to grips with structuring a web application for production using Neo4j. By the end of this book, you'll not only be able to harness the power of graphs to handle a broad range of problem areas, but you'll also have learned how to use Neo4j efficiently to identify complex relationships in your data. What you will learn Become well-versed with Neo4j graph database building blocks, nodes, and relationships Discover how to create, update, and delete nodes and relationships using Cypher querying Use graphs to improve web search and recommendations Understand graph algorithms such as pathfinding, spatial search, centrality, and community detection Find out different steps to integrate graphs in a normal machine learning pipeline Formulate a link prediction problem in the context of machine learning Implement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphs Who this book is for This book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.
Linear Algebra for Computational Sciences and Engineering
This book presents the main concepts of linear algebra from the viewpoint of applied scientists such as computer scientists and engineers, without compromising on mathematical rigor. Based on the idea that computational scientists and engineers need, in both research and professional life, an understanding of theoretical concepts of mathematics in order to be able to propose research advances and innovative solutions, every concept is thoroughly introduced and is accompanied by its informal interpretation. Furthermore, most of the theorems included are first rigorously proved and then shown in practice by a numerical example. When appropriate, topics are presented also by means of pseudocodes, thus highlighting the computer implementation of algebraic theory.It is structured to be accessible to everybody, from students of pure mathematics who are approaching algebra for the first time to researchers and graduate students in applied sciences who need a theoretical manual of algebra to successfully perform their research. Most importantly, this book is designed to be ideal for both theoretical and practical minds and to offer to both alternative and complementary perspectives to study and understand linear algebra.
Mongodb Topology Design
Create a world-class MongoDB cluster that is scalable, reliable, and secure. Comply with mission-critical regulatory regimes such as the European Union's General Data Protection Regulation (GDPR). Whether you are thinking of migrating to MongoDB or need to meet legal requirements for an existing self-managed cluster, this book has you covered. It begins with the basics of replication and sharding, and quickly scales up to cover everything you need to know to control your data and keep it safe from unexpected data loss or downtime.This book covers best practices for stable MongoDB deployments. For example, a well-designed MongoDB cluster should have no single point of failure. The book covers common use cases when only one or two data centers are available. It goes into detail about creating geopolitical sharding configurations to cover the most stringent data protection regulation compliance. The book also covers different tools and approaches for automating and monitoring a cluster with Kubernetes, Docker, and popular cloud provider containers. What You Will LearnGet started with the basics of MongoDB clustersProtect and monitor a MongoDB deploymentDeepen your expertise around replication and shardingKeep effective backups and plan ahead for disaster recoveryRecognize and avoid problems that can occur in distributed databasesBuild optimal MongoDB deployments within hardware and data center limitationsWho This Book Is ForSolutions architects, DevOps architects and engineers, automation and cloud engineers, and database administrators who are new to MongoDB and distributed databases or who need to scale up simple deployments. This book is a complete guide to planning a deployment for optimal resilience, performance, and scaling, and covers all the details required to meet the new set of data protection regulations such as the GDPR. This book is particularly relevant for large global organizations such as financial and medical institutions, as well as government departments that need to control data in the whole stack and are prohibited from using managed cloud services.
Computer Information Systems and Industrial Management
This book constitutes the proceedings of the 19th International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2020, held in Bialystok, Poland, in October 2020. Due to the COVID-19 pandemic the conference has been postponed to October 2020. The 40 full papers presented together with 5 abstracts of keynotes were carefully reviewed and selected from 62 submissions. The main topics covered by the chapters in this book are biometrics, security systems, multimedia, classification and clustering, industrial management. Besides these, the reader will find interesting papers on computer information systems as applied to wireless networks, computer graphics, and intelligent systems. The papers are organized in the following topical sections: biometrics and pattern recognition applications; computer information systems and security; industrial management and other applications; machine learning and high performance computing; modelling and optimization.
Business Process Management
Business process management is usually treated from two different perspectives: business administration and computer science. While business administration professionals tend to consider information technology as a subordinate aspect in business process management for experts to handle, by contrast computer science professionals often consider business goals and organizational regulations as terms that do not deserve much thought but require the appropriate level of abstraction.Matthias Weske argues that all communities involved need to have a common understanding of the different aspects of business process management. To this end, he details the complete business process lifecycle from the modeling phase to process enactment and improvement, taking into account all different stakeholders involved. After starting with a presentation of general foundations and abstraction models, he explains concepts like process orchestrations and choreographies, as well as process properties and data dependencies. Finally, he presents both traditional and advanced business process management architectures, covering, for example, workflow management systems, service-oriented architectures, and data-driven approaches. In addition, he shows how standards like WfMC, SOAP, WSDL, and BPEL fit into the picture.This textbook is ideally suited for classes on business process management, information systems architecture, and workflow management. This 3rd edition contains a new chapter on business decision modelling, covering the Decision Model and Notation (DMN) standard; the chapter on process choreographies has been streamlined, and numerous clarifications have been fetched throughout the book. The accompanying website www.bpm-book.com contains further information and additional teaching material.
Multimedia Technology and Enhanced Learning
This two-volume book constitutes the refereed proceedings of the Second International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2020, held in Leicester, United Kingdom, in April 2020. Due to the COVID-19 pandemic all papers were presented in YouTubeLive. The 83 revised full papers have been selected from 158 submissions. They describe new learning technologies which range from smart school, smart class and smart learning at home and which have been developed from new technologies such as machine learning, multimedia and Internet of Things.
Azure for Architects - Third EditionCreate secure, scalable, high-availability applications on the cloud
Build and design multiple types of applications that are cross-language, platform, and cost-effective by understanding core Azure principles and foundational concepts Key Features Get familiar with the different design patterns available in Microsoft Azure Develop Azure cloud architecture and a pipeline management system Get to know the security best practices for your Azure deployment Book Description Thanks to its support for high availability, scalability, security, performance, and disaster recovery, Azure has been widely adopted to create and deploy different types of application with ease. Updated for the latest developments, this third edition of Azure for Architects helps you get to grips with the core concepts of designing serverless architecture, including containers, Kubernetes deployments, and big data solutions. You'll learn how to architect solutions such as serverless functions, you'll discover deployment patterns for containers and Kubernetes, and you'll explore large-scale big data processing using Spark and Databricks. As you advance, you'll implement DevOps using Azure DevOps, work with intelligent solutions using Azure Cognitive Services, and integrate security, high availability, and scalability into each solution. Finally, you'll delve into Azure security concepts such as OAuth, OpenConnect, and managed identities. By the end of this book, you'll have gained the confidence to design intelligent Azure solutions based on containers and serverless functions. What you will learn Understand the components of the Azure cloud platform Use cloud design patterns Use enterprise security guidelines for your Azure deployment Design and implement serverless and integration solutions Build efficient data solutions on Azure Understand container services on Azure Who this book is for If you are a cloud architect, DevOps engineer, or a developer looking to learn about the key architectural aspects of the Azure cloud platform, this book is for you. A basic understanding of the Azure cloud platform will help you grasp the concepts covered in this book more effectively.
A Practical Guide to Database Design
This book contains a major update to the previous edition. It covers how to implement and manage the Database Management System (DBMS) itself, how to write scripts to extract and load data from source files, and how to develop user interfaces to view and update data within the database.
Data-Driven Process Discovery and Analysis
This book constitutes revised selected papers from the 8th and 9th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2018, held in Seville, Spain, on December 13-14, 2018, and SIMPDA 2019, held in Bled, Slovenia, on September 8, 2019. From 16 submissions received for SIMPDA 2018 and 9 submissions received for SIMPDA 2019, 3 papers each were carefully reviewed and selected for presentation in this volume. They cover theoretical issues related to process representation, discovery, and analysis or provide practical and operational examples of their application.
Statistical and Machine-Learning Data Mining:
Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
