Das Arbeitsergebnis des Studienkreises Dr. Parli wurde zunachst in Form eines internen Arbeitsberichtes den Mitgliedern des Foerderervereins des Betriebswirtschaftlichen Instituts fur Organisation und Automation an der Universitat zu Koeln (BIFOA) zur Verfugung gestellt. Die sich daraus er- gebende Diskussion zeigte, dass die Probleme der Istaufnahme bei automati- sierter Datenverarbeitung (ADV) nach wie vor in Wissenschaft und Praxis von hoher Aktualitat sind, so dass mir nunmehr - nicht zuletzt aufgrund zahlreicher Anfragen aus der Wirtschaftspraxis - eine Publikation in der Instituts-Schriftenreihe sinnvoll erscheint. Damit werden die Ergebnisse einem groesseren Interessentenkreis zuganglich und koennen insbesondere mittleren und kleineren Unternehmungen und Einheiten der oeffentlichen Verwaltung, die aufgrund des vielfaltigen Angebots unterschiedlicher Computergroessen ebenfalls in den Kreis der Anwender von Anlagen der automatisierten Datenverarbeitung geruckt sind, als Orientierungshilfe dienen. In der vorliegenden Arbeit werden die Erfahrungen von Wirtschaftsprakti- kern aus Grossunternehmungen verschiedener Branchen sowie der oeffent- lichen Verwaltung systematisiert und auf ihre Allgemeingultigkeit unter- sucht. Es handelt sich um Erfahrungen, die aus Unternehmungen stammen, die sich aufgrund ihres Geschaftsumfanges schon fruhzeitig zum Einsatz von ADV-Anlagen entschliessen mussten und die teilweise - entsprechend den Stufen der technischen und organisatorischen Entwicklung - mit den Istaufnahmeproblemen unterschiedlichster Art konfrontiert wurden. Das Hauptanliegen der Schrift besteht nicht in einer rein theoretischen Durchdringung des Problemkreises Istaufnahme und Automatisierte Daten- verarbeitung, sondern in einer praxisbezogenen Aufbereitung und Systema- tisierung empirischen Wissens auf diesem Gebiet.

Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory Key Features Learn how to load and transform data from various sources, both on-premises and on cloud Use Azure Data Factory's visual environment to build and manage hybrid ETL pipelines Discover how to prepare, transform, process, and enrich data to generate key insights Book DescriptionAzure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You'll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you'll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you'll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You'll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you'll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects. What you will learn Create an orchestration and transformation job in ADF Develop, execute, and monitor data flows using Azure Synapse Create big data pipelines using Azure Data Lake and ADF Build a machine learning app with Apache Spark and ADF Migrate on-premises SSIS jobs to ADF Integrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure Functions Run big data compute jobs within HDInsight and Azure Databricks Copy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectors Who this book is forThis book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You'll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is expected.

Data is an increasingly important business asset and enabler for organisational activities. Data quality is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability. This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.

Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboards Key Features Enhance your presentation skills by implementing engaging data storytelling and visualization techniques Learn the basics of machine learning and easily apply machine learning models to your data Improve productivity by automating your data processes Book DescriptionData Analytics Made Easy is an accessible beginner's guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling - Tired of people not listening to you and ignoring your results? Don't worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows - Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You'll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning - Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You'll not only be able to understand data scientists' machine learning models; you'll be able to challenge them and build your own. Creating interactive dashboards - Follow the book's simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results. What you will learn Understand the potential of data and its impact on your business Import, clean, transform, combine data feeds, and automate your processes Influence business decisions by learning to create engaging presentations Build real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and more Create professional-looking and business-centric visuals and dashboards Open the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning models Who this book is forThis book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers. No previous math, statistics, or computer science knowledge is required.

1m Oktober 1968 trafen Klinikchefs mit Spezialisten aus dem Bereich der Hoch- schulen und der Computer-lndustrie in Reinhartshausen zusammen, urn innerhalb der raschen Entwicklung der sogenannten zweiten technischen Revolution den Trend der modernen Medizin aufzusptiren. Ais Diskussionsgrundlage dienten ausgewillllte Refe- rate. Ein tiberblick tiber den Verlauf dieser Tagung Ui.l3t es niitzlich erscheinen, die Thematik einem grol3eren Kreis zugiinglich zu machen. So haben wir uns entschlossen, die Manuskripte der Autoren zu einem Werk zusammenzuschliel3en. Die technischen Grundlagen der elektronischen Datenverarbeitung sollen dabei allerdings unbertick- sichtigt bleiben. Die Durchsicht der Beitrage mag den Eindruck erwecken, dal3 anscheinend bereits zurtickliegende Entwicklungsphasen mit phantasievollen Forderungen an die Zukunft inhomogen zusammengestellt seien. Aber es kommt uns darauf an, in der bestaunens- wert en Schnelligkeit, mit der sich eine elektronische Informationsverarbeitung - oder besser formuliert - die moderne Wissenschaft der Informatik vollzieht, den gegen- wartigen Zustand in der Medizin aufzuzeigen und in ihm an den Einzelheiten die Ten- denzen darzustellen, die sich bald aus den ursprtinglichen mechanischen Formen der Erfassung und Verarbeitung von Daten, bald aus dem Bild der Zukunft deutlicher ab- zeichnen. Wir hegen die Hoffnung, dal3 auf dieser Basis sich pragende Konzeptionen fUr die Gestaltung der Zukunft ergeben. Herrn Kollegen NORBERT EICHENSEHER danken wir fUr seine wertvolle Unter- stiltzung bei den Korrekturen und der Abfassung des Sachverzeichnisses.

This report discusses the role computer-assisted personal interviewing (CAPI) can play in transforming survey data collection to allow better monitoring of the Sustainable Development Goals. The first part of this publication provides rigorous quantitative evidence on why CAPI is a better alternative to the traditional pen and paper interviewing method, particularly in the context of nationally representative surveys. The second part discusses the benefits of delivering CAPI training to statisticians using the popular massive online open course format. The final part provides a summary of existing CAPI platforms and offers some preliminary advice for NSOs to consider when selecting a CAPI platform for their institution. This is a Special Supplement to the Key Indicators for Asia and the Pacific 2019.

Als Stahl bezeichnet man heute alle Eisenlegierungen - mit Ausnahme der nicht schmiedbaren hochkohlenstoffhaltigen Gu sorten wie Grauguli, Hartguf und Ternperguf - ohne Riicksichr auf ihre Eigenschaften. Friiher wurde als wesentliches Merkmal des Stahles die Hartbarkeit angesehen. Es gibt aber eine ganze Reihe von Stahlen, die sich nicht harten lassen, die durch das Abschrecken aus hohen Temperaturen im Gegenteil sogar weicher, zaher werden. Edelstdble werden vielfach solche Stahle genannt, die au er mit Kohlenstoff auch noch mit anderen Grundstoffen, z. B. mit Chrom, Nickel, Wolfram, Vanadin usw. legiert sind. Diese Begriffsbestim- mung ist jedoch nicht erschopfend und auch anfechtbar, Denn man wird einen reinen Kohlenstoffstahl, der sorgfaltig erzeugt und auf dem ganzen Wege der Herstellung - vom Gu bis zum Versand - immer wieder gewissenhaft gepriift worden ist, zweifellos auch zu den Edelstahlen rechnen miissen. Andererseits enthalten manchmal Massenstahle - auch als unbeabsichtigte Verunreinigungen - ge- wisse Mengen von Legierungselementen. Das Richtige wird man treffen, wenn man die bei den grofsen Hiittenwerken in grofien Mengen erzeugten billigen Stahle als .Mas- senstahle bezeichnet, die von einem Edelstahlwerk mit Sorgfalt und unter scharfster Kontrolle hergestellten Stahle dagegen als Edelstahle. Die billigen Massenstahle werden meistens nach Festigkeit ver- kauft, die Edelstahle dagegen nach dem Verwendungszweck und unter einer Markenbezeichnung.

Leverage the Azure analytics platform's key analytics services to deliver unmatched intelligence for your data Key Features Learn to ingest, prepare, manage, and serve data for immediate business requirements Bring enterprise data warehousing and big data analytics together to gain insights from your data Develop end-to-end analytics solutions using Azure Synapse Book DescriptionAzure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform. The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features. By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks. What you will learn Explore the necessary considerations for data ingestion and orchestration while building analytical pipelines Understand pipelines and activities in Synapse pipelines and use them to construct end-to-end data-driven workflows Query data using various coding languages on Azure Synapse Focus on Synapse SQL and Synapse Spark Manage and monitor resource utilization and query activity in Azure Synapse Connect Power BI workspaces with Azure Synapse and create or modify reports directly from Synapse Studio Create and manage IP firewall rules in Azure Synapse Who this book is forThis book is for data architects, data scientists, data engineers, and business analysts who are looking to get up and running with the Azure Synapse Analytics platform. Basic knowledge of data warehousing will be beneficial to help you understand the concepts covered in this book more effectively.

Oberlegungen iiber die Automatisierung der Verwaltungstatigkeit in indu striellen Unternehmungen lie en vermuten, daB die verschiedenartigen ein zelnen Arbeiten auf eine gleichartige Grundfunktion zuriickgefiihrt werden konnen. Zu dieser Fragestellung gab Herr Professor Dr. Dr. Beste dankens werterweise die Anregung, die Untersuchung in der vorliegenden allgemei nen Fassung durchzufiihren. Der Verfasser hat sich bemiiht, in mehrjahriger praktischer Tatigkeit die dargestellten theoretischen Erkenntnisse aus den in der industriellen Praxis vorgefundenen Gegebenheiten heraus zu entwickeln. Bei der Durchsprache einzelner Probleme erhielt der Verfasser dariiber hin aus von Herrn Professor Dr. Dr. Beste und Herrn Professor Dr. von Kortz fleisch wertvolle Anregungen, fUr die er auch an dieser Stelle seinen beson deren Dank aussprechen mochte. Die Arbeit wurde im Rahmen des Industrieseminars der Universitat Koln angefertigt. Essen, den 1. November 1962 INHALT Seite 5 Vorwort I. Begriffe und Bereich einer betriebswirtschaftlichen Untersuchung uber die Information in der industriellen Unternehmung . . . . . . . 9 A. Unterschiedliche Produktivit1it bei der Materialverarbeitung und der Informationsverarbeitung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 B. Zum Wesen der Information als Tatigkeitsgegenstand in der industriellen Unternehmung und zur Kommunikation . . . . . . . 12 C. Bereich und Ziele der Untersuchung . . . . . . . . . . . . . . . . . . . . . . . . 14 II. Die Grundbausteine der Information und ihrer Verarbeitung . . . 17 A. Die elementare Struktur der Information . . . . . . . . . . . . . . . . . . . . 17 1. Der formale Gehalt der Information . . . . . . . . . . . . . . . . . . . . . . . 18 2. Der informative Gehalt cler Information . . . . . . . . . . . . . . . . . . . . 23 3. Die betriebswirtschaftliche MaBeinheit der Information . . . . . . . 26 B. Die elementaren Kommunikationswege . . . . . . . . . . . . . . . . . . . . . . 28 1. Die vertikale und die horizontale Anordnung der Kommunikationswege . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2. Das geschlossene Kommunikationssystem als grundsatzliche or- nisatorische Struktur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 C. Die elementaren Verarbeitungsvorgange . . . . . . . . . . . . . . . . . . . . ."

Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key Features Get to grips with the distributed training and deployment of machine learning and deep learning models Learn how ETLs are integrated with Azure Data Factory and Delta Lake Explore deep learning and machine learning models in a distributed computing infrastructure Book DescriptionMicrosoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you'll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you'll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you'll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline. What you will learn Create ETLs for big data in Azure Databricks Train, manage, and deploy machine learning and deep learning models Integrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creation Discover how to use Horovod for distributed deep learning Find out how to use Delta Engine to query and process data from Delta Lake Understand how to use Data Factory in combination with Databricks Use Structured Streaming in a production-like environment Who this book is forThis book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.

Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features Build, train, and deploy machine learning models quickly using Amazon SageMaker Analyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniques Improve productivity by training and fine-tuning machine learning models in production Book DescriptionAmazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You'll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you'll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You'll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you'll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learn Create and automate end-to-end machine learning workflows on Amazon Web Services (AWS) Become well-versed with data annotation and preparation techniques Use AutoML features to build and train machine learning models with AutoPilot Create models using built-in algorithms and frameworks and your own code Train computer vision and NLP models using real-world examples Cover training techniques for scaling, model optimization, model debugging, and cost optimization Automate deployment tasks in a variety of configurations using SDK and several automation tools Who this book is forThis book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.

Get to grips with building and productionizing end-to-end big data solutions in Azure and learn best practices for working with large datasets Key Features Integrate with Azure Synapse Analytics, Cosmos DB, and Azure HDInsight Kafka Cluster to scale and analyze your projects and build pipelines Use Databricks SQL to run ad hoc queries on your data lake and create dashboards Productionize a solution using CI/CD for deploying notebooks and Azure Databricks Service to various environments Book DescriptionAzure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse. The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You'll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you'll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD). By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps. What you will learn Read and write data from and to various Azure resources and file formats Build a modern data warehouse with Delta Tables and Azure Synapse Analytics Explore jobs, stages, and tasks and see how Spark lazy evaluation works Handle concurrent transactions and learn performance optimization in Delta tables Learn Databricks SQL and create real-time dashboards in Databricks SQL Integrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelines Discover how to use RBAC and ACLs to restrict data access Build end-to-end data processing pipeline for near real-time data analytics Who this book is forThis recipe-based book is for data scientists, data engineers, big data professionals, and machine learning engineers who want to perform data analytics on their applications. Prior experience of working with Apache Spark and Azure is necessary to get the most out of this book.