Databaser

Genealogies document relationships between persons involved in historical events. Information about the events is parsed from communications from the past. This book explores a way to organize information from multiple communications into a trustworthy representation of a genealogical history of the modern world. The approach defines metrics for evaluating the consistency, correctness, closure, connectivity, completeness, and coherence of a genealogy. The metrics are evaluated using a 312,000-person research genealogy that explores the common ancestors of the royal families of Europe. A major result is that completeness is defined by a genealogy symmetry property driven by two exponential processes, the doubling of the number of potential ancestors each generation, and the rapid growth of lineage coalescence when the number of potential ancestors exceeds the available population. A genealogy expands from an initial root person to a large number of lineages, which then coalesce into a small number of progenitors. Using the research genealogy, candidate progenitors for persons of Western European descent are identified. A unifying ancestry is defined to which historically notable persons can be linked.

This book constitutes the refereed proceedings of the International Joint Conference on Rough Sets, IJCRS 2023, held in Krakow, Poland, during October 5¿8, 2023.The 43 full papers included in this book were carefully reviewed and selected from 83 submissions. They were organized in topical sections as follows: Rough Set Models, Foundations, Three-way Decisions, Granular Models, Distances and Similarities, Hybrid Approaches, Applications, Cybersecurity and IoT.

This volume LNCS 14396 constitutes the refereed proceedings of the 12th International Conference, MEDI 2023,in November 2023 ,held in Sousse, Tunisia.The 27 full papers were carefully peer reviewed and selected from 99 submissions. The Annual International Conference on Model and Data Engineering focuses on bring together researchers and practitioners and enabling them to showcase the latest advances in modelling and data management.

The European Summer School in Logic, Language and Information (ESSLLI) is organized every year by the Association for Logic, Language and Information (FoLLI) in different sites around Europe. The papers cover vastly dierent topics, but each fall in the intersection of the three primary topics of ESSLLI: Logic, Language and Computation. The 13 papers presented in this volume have been selected among 81 submitted papers over the years 2019, 2020 and 2021. The ESSLLI Student Session is an excellent venue for students to present their work and receive valuable feedback from renowned experts in their respective fields. The Student Session accepts submissions for three different tracks: Language and Computation (LaCo), Logic and Computation (LoCo), and Logic and Language (LoLa).

This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by ¿family¿ to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.

This book explores the connections between qualitative data reuse, big social research, and data curation. A review of existing literature identifies the key issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. Through interviews of qualitative researchers, big social researchers, and data curators, the author further examines each key issue and produces new insights about how domain differences affect each community of practice¿s viewpoints, different strategies that researchers and curators use to ensure responsible practice, and different perspectives on data curation. The book suggests that encouraging connections between qualitative researchers, big social researchers, and data curators can support responsible scaling up of social research, thus enhancing discoveries in social and behavioral science.

An accessible guide to the practical steps of essential project management activities, including developing a plan, building risks and issues logs and managing a project through to completion.

With the growing advances in technology and transformation to digital services, the world is becoming more connected and more complex. Huge heterogeneous data are generated at rapid speed from various types of sensors. Augmented with artificial intelligence and machine learning and internet of things, latent relations, and new insights can be captured helping in optimizing plans and resource utilization, improving infrastructure, and enhancing quality of services.A ¿spatial data management system¿ is a way to take care of data that has something to do with space. This could include data such as maps, satellite images, and GPS data. A temporal data management system is a system designed to manage data that has a temporal component. This could include data such as weather data, financial data, and social media data. Some advanced techniques used in spatial and temporal data management systems include geospatial indexing for efficient querying and retrieval of location-based data, time-series analysis for understanding and predicting temporal patterns in datasets like weather or financial trends, machine learning algorithms for uncovering hidden patterns and correlations in large and complex datasets, and integration with Internet of Things (IoT) technologies for real-time data collection and analysis. These techniques, augmented with artificial intelligence, enable the extraction of latent relations and insights, thereby optimizing plans, improving infrastructure, and enhancing the quality of services. This book provides essential technical knowledge, best practices, and case studies on the state-of-the-art techniques of artificial intelligence and machine learning for spatiotemporal data analysis and modeling. The book is composed of several chapters written by experts in their fields and focusing on several applications including recommendation systems, big data analytics, supply chains and e-commerce, energy consumption and demand forecasting,and traffic and environmental monitoring. It can be used as academic reference at graduate level or by professionals in science and engineering related fields such as data science and engineering, big data analytics and mining, artificial intelligence, machine learning and deep learning, cloud computing, and internet of things.

Learn to create, index and query spatial databases using SQL that applies to PostgreSQL/PostGIS and more.

Empowering IoT with Big Data Analytics provides comprehensive coverage of major topics, tools, and techniques related to empowering IoT with big data technologies and big data analytics solutions, thus allowing for better processing, analysis, protection, distribution, and visualization of data for the benefit of IoT applications and second, a better deployment of IoT applications on the ground. This book covers big data in the IoT era, its application domains, current state-of-the-art in big data and IoT technologies, standards, platforms, and solutions. This book provides a holistic view of the big data value-chain for IoT, including storage, processing, protection, distribution, analytics, and visualization. Big data is a multi-disciplinary topic involving handling intensive, continuous, and heterogeneous data retrieved from different sources including sensors, social media, and embedded systems. The emergence of Internet of Things (IoT) and its application to many domains has led to the generation of huge amounts of both structured and unstructured data often referred to as big data.

Build faster and efficient real-world applications on the cloud with a fitting database model that's perfect for your needsKey FeaturesFamiliarize yourself with business and technical considerations involved in modeling the right databaseTake your data to applications, analytics, and AI with real-world examplesLearn how to code, build, and deploy end-to-end solutions with expert advicePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you'll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You'll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.What you will learnUnderstand different use cases and real-world applications of data in the cloudWork with document and indexed NoSQL databasesGet to grips with modeling considerations for analytics, AI, and MLUse real-world examples to learn about ETL servicesDesign structured, semi-structured, and unstructured data for your applications and analyticsImprove observability, performance, security, scalability, latency SLAs, SLIs, and SLOsWho this book is forThis book is for database developers, data engineers, and architects looking to design, model, and build database applications on the cloud with an extended focus on operational consideration and taking their data to AI. Data scientists, as well ML and AI engineers who want to use Google Cloud services in the data to AI journey will also find plenty of useful information in this book. It will also be useful to data analysts and BI developers who want to use SQL impactfully to generate ML and generative AI insights from their data. Table of ContentsData, Databases, and DesignHandling Data on the CloudDatabase Modeling for Structured DataSetting Up a Fully Managed RDBMSDesigning an Analytical Data WarehouseDesigning for Semi-structured DataUnstructured Data ManagementDevOps and DatabasesData to AI - Modeling Your Databases for Analytics and MLLooking Ahead - Designing for LLM Applications

Discover actionable steps to maintain healthy data pipelines to promote data observability within your teams with this essential guide to elevating data engineering practicesKey FeaturesLearn how to monitor your data pipelines in a scalable wayApply real-life use cases and projects to gain hands-on experience in implementing data observabilityInstil trust in your pipelines among data producers and consumers alikePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization.This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You'll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you'll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization.Equipped with the mastery of data observability intricacies, you'll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.What you will learnImplement a data observability approach to enhance the quality of data pipelinesCollect and analyze key metrics through coding examplesApply monkey patching in a Python moduleManage the costs and risks associated with your data pipelineUnderstand the main techniques for collecting observability metricsImplement monitoring techniques for analytics pipelines in productionBuild and maintain a statistics engine continuouslyWho this book is forThis book is for data engineers, data architects, data analysts, and data scientists who have encountered issues with broken data pipelines or dashboards. Organizations seeking to adopt data observability practices and managers responsible for data quality and processes will find this book especially useful to increase the confidence of data consumers and raise awareness among producers regarding their data pipelines.Table of ContentsFundamentals of Data Quality MonitoringFundamentals of Data ObservabilityData Observability techniquesData Observability elementsDefining rules on indicatorsRoot cause analysisOptimizing data pipelinesIntroducing and changing culture in the team Data observability checklistUse Cases

A quiet revolution is taking place within the computer ecosystem; one that will change the way we do business on the internet.It's called blockchain, and it promises to disrupt the way people interact with one another online, whether its messaging, banking, keeping up with medical records, land records, booking a vacation, socializing, or voting. Programs are also being developed to use blockchain to serve as one's identity "papers."Blockchain technology is based on the idea that all online transactions should be between two people without the need for public or private third-party oversight. Blockchain technology developers believe thoughts and ideas should be shared, not quashed. It's a world where web platforms are governed by their members, not a board of directors; privacy comes first, and one's personal information is kept private, not for third parties to take and sell as they, please.Blockchain technology offers everyone opportunities to take part. Anyone can participate in the fast-growing world using non-fungible tokens (NFTs), where works of art, music, literature, and poetry can be tokenized and sold or traded on a blockchain.These components comprise the next generation of the world wide web, which is referred to as Web 3.0. Billions of dollars are being spent to create infrastructure to create a viable framework to mainstream blockchain.This book offers a peek into this new world with examples of how this technology is being used today as well as the hurdles, including legal challenges, it must overcome to be viable.So, if you're ready, let's meet blockchain.

Bienvenue dans Blockchain : Démystifier la technologie Blockchain - Un guide complet des systèmes décentralisés . La technologie blockchain est devenue une force puissante à l'ère numérique moderne, révolutionnant plusieurs secteurs et bouleversant les structures établies. Les entreprises, les gouvernements et les particuliers du monde entier s'y intéressent en raison de sa structure décentralisée, de son immuabilité et de sa sécurité. Ce guide détaillé vise à démystifier la technologie blockchain et à vous fournir une compréhension approfondie de ses idées, éléments et utilisations fondamentaux. Cet ebook vous servira de référence pour comprendre la complexité de cette technologie révolutionnaire, que vous soyez un débutant curieux de la blockchain ou un professionnel chevronné essayant d'approfondir vos connaissances.Nous entreprendrons un voyage dans le monde de la blockchain dans les chapitres suivants, en commençant par un résumé de son contexte et de ses principales caractéristiques. Dans cet ebook, nous examinerons les idées fondamentales qui sous-tendent la blockchain, telles que la technologie du grand livre distribué, la cryptographie, les règles de consensus et les contrats intelligents.

Discover the multi-model capabilities of Redis Stack as a document store and vector database, with support for time series, stream processing, probabilistic data structures, and moreKey FeaturesModel, index, and search data using JSON and vector data typesModernize your applications with vector similarity search, documents hybrid search, and moreConfigure a scalable, highly available, and secure server using RedisInsightPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn modern applications, efficiency in both operational and analytical aspects is paramount, demanding predictable performance across varied workloads. This book introduces you to Redis Stack, an extension of Redis and guides you through its broad data modeling capabilities. With practical examples of real-time queries and searches, you'll explore Redis Stack's new approach to providing a rich data modeling experience all within the same database server.You'll learn how to model and search your data in the JSON and hash data types and work with features such as vector similarity search, which adds semantic search capabilities to your applications to search for similar texts, images, or audio files. The book also shows you how to use the probabilistic Bloom filters to efficiently resolve recurrent big data problems. As you uncover the strengths of Redis Stack as a data platform, you'll explore use cases for managing database events and leveraging introduce stream processing features. Finally, you'll see how Redis Stack seamlessly integrates into microservices architectures, completing the picture.By the end of this book, you'll be equipped with best practices for administering and managing the server, ensuring scalability, high availability, data integrity, stored functions, and more.What you will learnGet started with data modeling using JSON and hash documents, vectors, and time seriesImplement microservices patterns and resolve typical use casesUse probabilistic data structures to analyze datasets and streamsIntegrate Redis Stack into your Java, Python, C#, Golang, and Node.js projectsDevelop powerful data flows using triggers and stream processing featuresManage your database server for scalability, high availability, and securityVisualize and manage your data with RedisInsightWho this book is forThis book is for database administrators, database developers, software developers, and software architects who want to discover the powerful real-time, multi-model capabilities of the Redis Stack database. A basic understanding of Redis and databases in general, coupled with software development skills in at least one of Java, Python, C#, Golang, or JavaScript languages will help you understand the concepts covered in this book.Table of ContentsIntroducing Redis StackResolving Use Cases with Redis StackGetting Started with Redis StackSetting Up Client LibrariesRedis Stack as a document storeRedis Stack as a vector databaseRedis Stack as a time series databaseUnderstanding Probabilistic Data StructuresProgrammability of Redis StackRedisInsight: the Data Management GUIUsing Redis Stack as a primary databaseManaging development and production environments

Be part of the future of Web3, decoding blockchain data to build trust in the next-generation internetKey Features:Build a deep understanding of the fundamentals of blockchain analyticsExtract actionable business insights by modeling blockchain dataShowcase your work and gain valuable experience to seize opportunities in the Web3 ecosystemPurchase of the print or Kindle book includes a free PDF eBookBook Description:Data is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3.You'll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You'll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data.The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you'll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients.By the end of this book, you'll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What You Will Learn:Understand the core components of blockchain transactions and blocksIdentify reliable sources of on-chain and off-chain data to build robust datasetsUnderstand key Web3 business questions and how data science can offer solutionsBuild your skills to create and query NFT- and DeFi-specific datasetsImplement a machine learning toolbox with real-world use cases in the Web3 spaceWho this book is for:This book is designed for data professionals-data analysts, data scientists, or data engineers- and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience.

This book presents a set of models, methods, and techniques that allow the successful implementation of data governance (DG) in an organization and reports real experiences of data governance in different public and private sectors.To this end, this book is composed of two parts. Part I on ¿Data Governance Fundamentals¿ begins with an introduction to the concept of data governance that stresses that DG is not primarily focused on databases, clouds, or other technologies, but that the DG framework must be understood by business users, systems personnel, and the systems themselves alike. Next, chapter 2 addresses crucial topics for DG, such as the evolution of data management in organizations, data strategy and policies, and defensive and offensive approaches to data strategy. Chapter 3 then details the central role that human resources play in DG, analysing the key responsibilities of the different DG-related roles and boards, while chapter 4 discusses the most common barriers to DG in practice. Chapter 5 summarizes the paradigm shifts in DG from control to value creation. Subsequently chapter 6 explores the needs, characteristics and key functionalities of DG tools, before this part ends with a chapter on maturity models for data governance. Part II on ¿Data Governance Applied¿ consists of five chapters which review the situation of DG in different sectors and industries. Details about DG in the banking sector, public administration, insurance companies, healthcare and telecommunications each are presented in one chapter.The book is aimed at academics, researchers and practitioners (especially CIOs, Data Governors, or Data Stewards) involved in DG. It can also serve as a reference for courses on data governance in information systems.

This book offers the latest research on formation of modern workers in the context of digitalization: from constant improvement of new training technologies to growing requirements for employees in workplace, as well as an assessment of specialists' digital skills and dynamics of changes over the past years. The book proposes to look at the pool of these problems through the prism of acquisition of new digital competencies and requirements for a modern worker.This book is intended for both young and experienced scientists who want to get acquainted with latest trends and data to use them in their research.

¿Der Einzelhandel hat sich durch die Digitalisierung in den letzten Jahren dramatisch gewandelt: Das Aufkommen des E-Commerce und die Verbreitung von Smartphones haben das Einkaufsverhalten der Verbraucher revolutioniert. Die Kundinnen und Kunden erwarten heute nicht nur Flexibilität und Bequemlichkeit beim Einkaufen, sondern auch Einkaufserlebnisse, bei denen sie online recherchieren, Produkte in physischen Geschäften ausprobieren und schließlich über die von ihnen bevorzugten Kanäle kaufen können. Die Integration verschiedener Vertriebskanäle ist somit der Schlüssel zum Erfolg im Einzelhandel in der heutigen digitalen Welt. Omnichannel versetzt Einzelhändler in die Lage, diese Erwartungen zu erfüllen, indem sie eine konsistente Markenpräsenz und einen reibungslosen Übergang zwischen den Kanälen gewährleisten.Dieses Buch vermittelt einen praxisnahen und leicht verständlichen Einblick über den Omnichannel-Handel und zeigt Einzelhändlern einen Weg auf, wie sie das Konzept erfolgreich in ihrem Unternehmen implementieren können. Neben einem kurzen Überblick über das veränderte Kaufverhalten von Kundinnen und Kunden erläutern die Autoren die Grundlagen von Omnichannel und zeigen verschiedene Strategien auf. Best Practices erfolgreicher Unternehmen geben wertvolle Tipps und Erkenntnisse, um bewährte Omnichannel-Strategien in die eigenen Geschäftsmodelle zu adaptieren.

In diesem Buch werden die wichtigsten rechtlichen Begriffe, die der ärztliche Gutachter kennen sollte, übersichtlich, kompakt und vor allem präzise erläutert. Derselbe Begriff kann in Medizin und Rechtswissenschaft durchaus unterschiedlich verstanden werden. So ist die interdisziplinäre Kommunikation zwischen ärztlichen Sachverständigen und Gerichten/Verwaltung/Versicherungen eine schwierige Aufgabe für alle Beteiligten. Viele Streitfragen könnten allerdings vermieden werden, wenn man gerade bei diesen von Juristen und Medizinern gemeinsam benutzten Begriffen nicht aneinander vorbeireden würde. Hier setzt das Buch an: Für alle nachvollziehbar und dennoch mit höchster Präzision werden die wichtigsten Begriffe ¿ sortiert von A ¿ Z ¿ definiert. Stolpersteine ¿ ¿Cave¿ ¿ werden thematisiert und hervorgehoben. Darüber hinaus runden Hinweise auf die Pflichten des ärztlichen Gutachters und zahlreiche Tipps für die Erstellung und Abrechnung von ärztlichen Gutachten das Werk ab.Der Schwerpunkt der Erläuterungen betrifft den orthopädisch-unfallchirurgischen Bereich, den Herr Dr. Ludolph vertritt.In der 3. Auflage werden folgende Neuerungen berücksichtigt:· Zum 01.01.2024 ist das SGB XIV mit neuen Anspruchsgrundlagen und bisher in der Sozialgesetzgebung nicht bekannten Beweiserleichterungen in Kraft getreten· Ab 2021 wurde das Justizvergütungs- und Entschädigungsgesetz geändert. Die Vergütung des ärztlichen Sachverständigen, die auch Grundlage zahlreicher Vergütungsregelungen außerhalb des Gesetzes ist, wurde erhöht, die Zuordnung der Leistungen des ärztlichen Sachverständigen zu den einzelnen Vergütungsgruppen (M1 bis M3) wurde geändert.· Zum 01.07.2023 wurde die Vergütung von Gutachten für Berufsgenossenschaften und Unfallkassen erhöht (UV-GOÄ).· Entfallen ist zum 01.01.2021 der Unterlassungszwang. 9 der 80 Berufskrankheiten sahen als Tatbestandsvoraussetzung die Unterlassung der als ursächlich für die Erkrankung zu diskutierende Tätigkeit voraus. Ersetzt wurde dieser durch Mitwirkungspflichten. · Berücksichtigt wurden zudem eine Vielzahl rechtlicher Änderungen und aktuelle Gerichtsentscheidungen.

In diesem Fachbuch wendet sich der sehr erfahrene Autor an junge Manager und Nachwuchs-Führungskräfte. Auf Basis seiner langjährigen Erfahrungen teilt er praxisnahe Empfehlungen zu einer Vielzahl von Führungsthemen und zu verschiedenen Situationen im beruflichen Alltag einer Führungskraft.Aus eigener Erfahrung weiß Egon Minar, dass Universitäten und Fachhochschulen zwar theoretisches Wissen vermitteln, die Umsetzung in die Praxis jedoch nicht trivial ist. In 55 kurzen Kapiteln greift er daher Themen auf, die ihn während seines Berufslebens begleitet haben und die er nun weitergeben möchte. Da Mitarbeiterführung für ihn zu den zentralen Aufgaben einer Führungskraft gehören, widmet er diesem Themenkomplex besondere Aufmerksamkeit.Ergänzend zu seinen praxisorientierten Ratschlägen, verweist der Autor für komplexe und besonders wichtige Führungsaufgaben auf entsprechende weiterführende Fachliteratur.

¿Das beginnende Zeitalter der künstlichen Intelligenz braucht menschliche Kompetenzen, wenn es erfolgreich gelingen soll. Anhand von aktuellen KI-basierten Beispielen beleuchtet das esssential den Zusammenhang von technologischer Entwicklung und menschlichen Kompetenzen, die sich nicht an die KI delegieren lassen: den Metaskills.Die Autoren beschreiben ausgewählte Metaskills, die einen reflektierten Umgang mit KI auf individueller, unternehmerischer und globaler Ebene ermöglichen und benennen jeweils konkrete Anwendungsfälle. Zudem erläutern sie zentrale Prinzipien, die nötig sind, um KI-Technologien menschengerecht zu entwickeln.

In dieser Studie werden die sozialen Praktiken im Beobachtungs- und Dokumentationsverfahren des Early Excellence-Ansatzes erforscht. Dabei werden im Rahmen einer (ko-)konstruktivistisch-postmodern ausgerichtete Ethnographie sowohl Beobachtungs- und Dokumentationspraktiken der pädagogischen Fachkräfte als auch der forschenden Person untersucht. Bezugnehmend auf praxistheoretische und beobachtungstheoretische Perspektiven wird die Komplexität der Organisation des Beobachtungs- und Dokumentationsverfahrens in der Praxis von Kindertageseinrichtungen herausgearbeitet und nachgezeichnet, wie die soziale Position in der Beobachtung relational zwischen programmatischen und pädagogischen Anforderungen hergestellt wird.