This book constitutes the thoroughly refereed post-conference proceedings of the Third COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2017, held in Gdansk, Poland, in September 2017. The 13 revised full papers and 5 short papers included in the first part of the book were carefully reviewed and selected from numerous submissions. The second part contains reports that summarize the major activities and achievements that have taken place in the context of the action: the short term scientific missions, the outcome of the summer schools, and the results achieved within the following four work packages: representation of structured data sources; keyword search; user interaction and keyword query interpretation; and research integration, showcases, benchmarks and evaluations. Also included is a short report generated by the chairs of the action. The papers cover a broad range of topics in the area of keyword search combining expertise from many different related fields such as information retrieval, natural language processing, ontology management, indexing, semantic web and linked data.

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 DescriptionThanks 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 forIf 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.

The new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself. In addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media, and discusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects. It covers advanced topics, including data monitoring and testing. Although the book includes an extra 100 pages worth of valuable content, the price has actually been reduced from $65 to $55.

Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now! Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyse text. Master these ten objectives: Build an unstructured data warehouse using the 11-step approach; Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure; Overcome challenges including blather, the Tower of Babel, and lack of natural relationships; Avoid the Data Junkyard and combat the "Spiders Web"; Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0 , including iterative development; Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement; Design the Document Inventory system and link unstructured text to structured data; Leverage indexes for efficient text analysis and taxonomies for useful external categorisation; Manage large volumes of data using advanced techniques such as backward pointers; Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances.

Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Nowadays, knowledge-based management systems include data warehouses as their core components. The purpose of building a data warehouse is twofold. Firstly, to integrate multiple heterogeneous, autonomous, and distributed data sources within an enterprise. Secondly, to provide a platform for advanced, complex, and efficient data analysis. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed among others for discovering trends, patterns of behavior, and anomalies as well as for finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data that more and more often come from WEB-based, XML-based, spatio-temporal, object, and multimedia systems, make data integration and processing challenging.

The objective of NEW TRENDS IN DATA WAREHOUSING AND DATA ANALYSIS is fourfold: First, to bring together the most recent research and practical achievements in the DW and OLAP technologies. Second, to open and discuss new, just emerging areas of further development. Third, to provide the up-to-date bibliography of published works and the resource of research achievements for anyone interested in up-to-date data warehouse issues. And, finally, to assist in the dissemination of knowledge in the field of advanced DW and OLAP.

Until recently, many people thought big data was a passing fad. "Data science" was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you'll appreciate how data science is fundamentally altering our world, for better and for worse. Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you'll find out how far data science reaches. With this anthology, you'll learn how: Analysts can now get results from their data queries in near real time Indie manufacturers are blurring the lines between hardware and software Companies try to balance their desire for rapid innovation with the need to tighten data security Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center CIOs have gradually evolved from order takers to business innovators New analytics tools let businesses go beyond data analysis and straight to decision-making Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries.

A practical guide to making good decisions in a world of missing data In the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don't see. Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don't know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions. Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones.

Big Data Imperatives, focuses on resolving the key questions on every one's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however the real value of big data is not in the overwhelming size of it, but more in its effective use. Your goal may be to obtain insight from voluminous data, with billions of loosely-structured bytes of data coming from different channels spread across different locations, which needs to be processed until the needle in the haystack is found.This book addresses the following big data characteristics: * Very large, distributed aggregations of loosely structured data -- often incomplete and inaccessible * Petabytes/Exabytes of data * Millions/billions of people providing/contributing to the context behind the data * Flat schema's with few complex interrelationships * Involves time-stamped events * Made up of incomplete data * Includes connections between data elements that must be probabilistically inferred Big data imperatives, explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform, to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big data imperatives, describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other.This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book, can also be used as a handbook for practitioners; helping them on methodology, technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data. What you'll learn * Understanding the technology, implementation of big data platforms and their usage for analytics * Big data architectures * Big data design patterns * Implementation best practices Who this book is for This book is designed for IT professionals, data warehousing, business intelligence professionals, data analysis professionals, architects, developers and business users

The two-volume set LNCS 6496 and 6497 constitutes the refereed proceedings of the 9th International Semantic Web Conference, ISWC 2010, held in Shanghai, China, during November 7-11, 2010. Part I contains 51 papers out of 578 submissions to the research track. Part II contains 18 papers out of 66 submissions to the semantic Web in-use track, 6 papers out of 26 submissions to the doctoral consortium track, and also 4 invited talks. Each submitted paper were carefully reviewed. The International Semantic Web Conferences (ISWC) constitute the major international venue where the latest research results and technical innovations on all aspects of the Semantic Web are presented. ISWC brings together researchers, practitioners, and users from the areas of artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, natural language processing, soft computing, and human computer interaction to discuss the major challenges and proposed solutions, the success stories and failures, as well the visions that can advance research and drive innovation in the Semantic Web.

Data warehousing and knowledge discovery are increasingly becoming mission-critical technologies for most organizations, both commercial and public, as it becomes incre- ingly important to derive important knowledge from both internal and external data sources. With the ever growing amount and complexity of the data and information available for decision making, the process of data integration, analysis, and knowledge discovery continues to meet new challenges, leading to a wealth of new and exciting research challenges within the area. Over the last decade, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has established itself as one of the most important international scientific events within data warehousing and knowledge discovery. DaWaK brings together a wide range of researchers and practitioners working on these topics. The DaWaK conference series thus serves as a leading forum for discu- ing novel research results and experiences within data warehousing and knowledge th discovery. This year's conference, the 11 International Conference on Data Wa- housing and Knowledge Discovery (DaWaK 2009), continued the tradition by d- seminating and discussing innovative models, methods, algorithms, and solutions to the challenges faced by data warehousing and knowledge discovery technologies.

Business intelligence (BI) used to be so simple -- in theory anyway. Integrate and copy data from your transactional systems into a specialised relational database, apply BI reporting and query tools and add business users. Job done. No longer. Analytics, big data and an array of diverse technologies have changed everything. More importantly, business is insisting on ever more, ever faster from information and from IT in general. An emerging biz-tech ecosystem demands that business and IT work together. This book reflects the new reality that in todays socially complex and rapidly changing world, business decisions must be based on a combination of rational and intuitive thinking. Integrating cues from diverse information sources and tacit knowledge, decision makers create unique meaning to innovate heuristically at the speed of thought. This book provides a wealth of new models that business and IT can use together to design support systems for tomorrows successful organisations. Dr Barry Devlin, one of the earliest proponents of data warehousing, goes back to basics to explore how the modern trinity of information, process and people must be reinvented and restructured to deliver the value, insight and innovation required by modern businesses. From here, he develops a series of novel architectural models that provide a new foundation for holistic information use across the entire business. From discovery to analysis and from decision making to action taking, he defines a fully integrated, closed-loop business environment. Covering every aspect of business analytics, big data, collaborative working and more, this book takes over where BI ends to deliver the definitive framework for information use in the coming years.

Cutting-edge content and guidance from a data warehousing expert--now expanded to reflect field trends

Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of "Data Warehousing Fundamentals," numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field.

The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.

This practical "Second Edition" highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information.

Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, "Data Warehousing Fundamentals" presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.

Provides the fundamentals, technologies, and best practices in designing, constructing and managing mission critical, energy efficient data centers Organizations in need of high-speed connectivity and nonstop systems operations depend upon data centers for a range of deployment solutions. A data center is a facility used to house computer systems and associated components, such as telecommunications and storage systems. It generally includes multiple power sources, redundant data communications connections, environmental controls (e.g., air conditioning, fire suppression) and security devices. With contributions from an international list of experts, The Data Center Handbook instructs readers to: * Prepare strategic plan that includes location plan, site selection, roadmap and capacity planning * Design and build "green" data centers, with mission critical and energy-efficient infrastructure * Apply best practices to reduce energy consumption and carbon emissions * Apply IT technologies such as cloud and virtualization * Manage data centers in order to sustain operations with minimum costs * Prepare and practice disaster reovery and business continuity plan The book imparts essential knowledge needed to implement data center design and construction, apply IT technologies, and continually improve data center operations.

Learn data architecture essentials and prepare for the Salesforce Certified Data Architect exam with the help of tips and mock test questions Key Features * Leverage data modelling, Salesforce database design, and techniques for effective data design * Learn master data management, Salesforce data management, and how to include considerations * Get to grips with large data volumes, performance tuning, and poor performance mitigation techniques Book Description The Salesforce Data Architect is a prerequisite exam for the Application Architect half of the Salesforce Certified Technical Architect credential. This book offers a complete, up-to-date coverage of the Salesforce Data Architect exam so you can take it with confidence. The book is written in a clear, succinct way with self-assessment and practice exam questions, covering all topics necessary to help you pass the exam with ease. You'll understand the theory around Salesforce data modeling, database design, master data management (MDM), Salesforce data management (SDM), and data governance. Additionally, performance considerations associated with large data volumes will be covered. You'll also get to grips with data migration and understand the supporting theory needed to achieve Salesforce Data Architect certification. By the end of this Salesforce book, you'll have covered everything you need to pass the Salesforce Data Architect certification exam and have a handy, on-the-job desktop reference guide to re-visit the concepts. What you will learn * Understand the topics relevant to passing the Data Architect exam * Explore specialist areas such as large data volumes * Test your knowledge with the help of exam-like questions * Pick up useful tips and tricks that can be referred to time and again * Understand the reasons underlying the way Salesforce data management works * Discover the techniques that are available for loading massive amounts of data Who This Book Is For This book is for both aspiring Salesforce data architects and those already familiar with Salesforce data architecture who want to pass the exam and have a reference guide to revisit the material as part of their day-to-day job. Working knowledge of the Salesforce platform is assumed, alongside a clear understanding of Salesforce architectural concepts.

Wettbewerbsvorteile werden in Zukunft nur noch die Unternehmen erlangen, denen es gelingt, Informationen in Wissen zu verwandeln. Die zwei Welten Business Intelligence und Knowledge Management wachsen vor diesem Hintergrund zusammen. Der Herausgeber, Leiter des Instituts fur Managementinformationssysteme und des Instituts fur Knowledge Management, zeigt in diesem Buch die zunehmende Integration der beiden Bereiche. Das Buch bringt damit Transparenz in einen der groessten IT-Wachstumsmarkte. Mehrere Studien, etwa des Fraunhofer Instituts, beleuchten den relevanten Markt und geben wichtige Orientierungshilfen. Anhand einer Vielzahl von Beispielen wird gezeigt, welchen Nutzen der Einsatz hochentwickelter Analysewerkzeuge und die Entwicklung von Loesungen fur das Wissensmanagement heute bereits erbringen. Ebenfalls sehr hilfreich fur Praktiker ist die umfangreiche Anbieterliste. Einen raschen UEberblick uber die wichtigsten KM- und BI-Begriffe bietet ferner das integrierte Glossar.

Data Warehousing ist seit einigen Jahren in vielen Branchen ein zentrales Thema. Die anfangliche Euphorie tauschte jedoch daruber hinweg, dass zur praktischen Umsetzung gesicherte Methoden und Vorgehensmodelle fehlten. Dieses Buch stellt einen Beitrag zur UEberwindung dieser Lucke zwischen Anspruch und Wirklichkeit dar. Es gibt im ersten Teil einen UEberblick uber aktuelle Ergebnisse im Bereich des Data Warehousing mit einem Fokus auf methodischen und betriebswirtschaftlichen Aspekten. Es finden sich u.a. Beitrage zur Wirtschaftlichkeitsanalyse, zur organisatorischen Einbettung des Data Warehousing, zum Datenqualitatsmanagement, zum integrierten Metadatenmanagement und zu datenschutzrechtlichen Aspekten sowie ein Beitrag zu moeglichen zukunftigen Entwicklungsrichtungen des Data Warehousing. Im zweiten Teil berichten Projektleiter umfangreicher Data Warehousing-Projekte uber Erfahrungen und Best Practices.

Problemloesungen fur das Top-Management: Das Buch stellt speziell fur Entscheidungstrager die Nutzungsmoeglichkeiten von Data-Warahouse-Konzepten vor. Neben den Grundlagen werden vor allem die Einsatzgebiete, verfugbare Loesungen und praktische Erfahrungen beschrieben. Das Management speziell aus Konsumguterindustrie und -handel erhalt so die Moeglichkeit, fur das eigene Unternehmen die optimale Entscheidung zu treffen.

Develop the must-have skills required for any data scientist to get the best results from Azure Databricks. Key Features * Learn to develop and productionize ML pipelines using the Databricks Unified Analytics platform * See how to use AutoML, Feature Stores, and MLOps with Databricks * Get a complete understanding of data governance and model deployment Book Description In this book, you'll get to grips with Databricks, enabling you to power-up your organization's data science applications. We'll walk through applying the Databricks AI and ML stack to real-world use cases for natural language processing, computer vision, time series data, and more. We'll dive deep into the complete model development life cycle for data ingestion and analysis, and get familiar with the latest offerings of AutoML, Feature Store, and MLStudio, on the Databricks platform. You'll get hands-on experience implementing repeatable ML operations (MLOps) pipeline using MLFlow, track model training and key metrics, and explore real-time ML, anomaly detection, and streaming analytics with Delta lake and Spark Structured Streaming. Starting with an overview of Data Science use cases across different organizations and industries, you will then be introduced to feature stores, feature tables, and how to access them. You will see why AutoML is important and how to create a baseline model with AutoML within Databricks. Utilizing the ML Flow model registry to manage model versioning and transition to production will be covered, along with detecting and protecting against model drift in production environments. By the end of the book, you will know how to set up your Databricks ML development and deployment as a CI/CD pipeline. What you will learn * Perform natural language processing, computer vision, and more * Explore AutoML, Feature Store, and MLStudio on Databricks * Dive deep into the complete model development life cycle * Experience implementing repeatable MLOps pipelines using MLFlow * Track model training and key metrics * Explore real-time ML, anomaly detection, and streaming analytics * Learn how to handle model drift Who This Book Is For In this book we are going to specifically focus on the tools catering to the Data Scientist persona. Readers who want to learn how to successfully build and deploy end-end Data Science projects using the Databricks cloud agnostic unified analytics platform will benefit from this book, along with AI and Machine Learning practitioners.

Supercharge and deploy Amazon Redshift Serverless, train and deploy Machine learning Models using Amazon Redshift ML and run inference queries at scale. Key Features * Learn to build Multi-Class Classification Models * Create a model, validate a model and draw conclusion from K-means clustering * Learn to create a SageMaker endpoint and use that to create a Redshift ML Model for remote inference Book Description Amazon Redshift Serverless enables organizations to run PetaBytes scales Cloud data warehouses in minutes and in most cost effective way Developers, data analysts and BI analysts can deploy cloud data warehouses and use easy-to-use tools to train models and run predictions. Developers working with Amazon Redshift data warehouses will be able to put their SQL knowledge to work with this practical guide to train and deploy Machine Learning Models. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin Deploying and Using Amazon Redshift Serverless and then dive into learning and deploying various types of Machine learning projects using familiar SQL Code. You will learn how to configure and deploy Amazon Redshift Serverless, understand the foundations of data analytics and types of data machine learning. Then you will deep dive into Redshift ML By the end of this book, you will be able to configure and deploy Amazon Redshift Serverless, train and deploy Machine learning Models using Amazon Redshift ML and run inference queries at scale. What you will learn * Learn how to implement an end-to-end serverless architecture for ingestion, analytics and machine learning using Redshift Serverless and Redshift ML * Learn how to create supervised and unsupervised models, and various techniques to influence your model * Learn how to run inference queries at scale in Redshift to solve a variety of business problems using models created with Redshift ML or natively in Amazon SageMaker * Learn how to optimize your Redshift data warehouse for extreme performance * Learn how to ensure you are using proper security guidelines with Redshift ML * Learn how to use model explainability in Amazon Redshift ML, to help understand how each attribute in your training data contributes to the predicted result. Who This Book Is For Data Scientists and Machine Learning developers who work with Amazon Redshift and want to explore it's machine learning capabilities will find this definitive guide helpful. Basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to get the best from this book.