Databaser

"This book delves into the practical applications of vector search in Elastic and embodies a broader philosophy. It underscores the importance of search in the age of Generative Al and Large Language Models. This narrative goes beyond the 'how' to address the 'why' - highlighting our belief in the transformative power of search and our dedication to pushing boundaries to meet and exceed customer expectations." Shay Banon Founder & CTO at ElasticKey FeaturesInstall, configure, and optimize the ChatGPT-Elasticsearch plugin with a focus on vector dataLearn how to load transformer models, generate vectors, and implement vector search with ElasticDevelop a practical understanding of vector search, including a review of current vector databasesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWhile natural language processing (NLP) is largely used in search use cases, this book aims to inspire you to start using vectors to overcome equally important domain challenges like observability and cybersecurity. The chapters focus mainly on integrating vector search with Elastic to enhance not only their search but also observability and cybersecurity capabilities.The book, which also features a foreword written by the founder of Elastic, begins by teaching you about NLP and the functionality of Elastic in NLP processes. Here you'll delve into resource requirements and find out how vectors are stored in the dense-vector type along with specific page cache requirements for fast response times. As you advance, you'll discover various tuning techniques and strategies to improve machine learning model deployment, including node scaling, configuration tuning, and load testing with Rally and Python. You'll also cover techniques for vector search with images, fine-tuning models for improved performance, and the use of clip models for image similarity search in Elasticsearch. Finally, you'll explore retrieval-augmented generation (RAG) and learn to integrate ChatGPT with Elasticsearch to leverage vectorized data, ELSER's capabilities, and RRF's refined search mechanism.By the end of this NLP book, you'll have all the necessary skills needed to implement and optimize vector search in your projects with Elastic.What you will learnOptimize performance by harnessing the capabilities of vector searchExplore image vector search and its applicationsDetect and mask personally identifiable informationImplement log prediction for next-generation observabilityUse vector-based bot detection for cybersecurityVisualize the vector space and explore Search.Next with ElasticImplement a RAG-enhanced application using StreamlitWho this book is forIf you're a data professional with experience in Elastic observability, search, or cybersecurity and are looking to expand your knowledge of vector search, this book is for you. This book provides practical knowledge useful for search application owners, product managers, observability platform owners, and security operations center professionals. Experience in Python, using machine learning models, and data management will help you get the most out of this book.Table of ContentsIntroduction to Vectors and EmbeddingsGetting started with Vector Search in ElasticModel Management and Vector Considerations in ElasticPerformance Tuning - Working with dataImage SearchRedacting Personal Identifiable Information Using ElasticsearchNext Generation of Observability Powered by VectorsThe Power of Vectors and Embedding in Bolstering Cybersecurity(N.B. Please use the Look Inside option to see further chapters)

The Importance of Data in Health CareIn the world of health care, data plays a crucial role in ensuring the delivery of efficient and effective services. From patient records to clinical trials, numerical data is invaluable in shaping policies, making informed decisions, and improving health outcomes. As students embarking on a journey in data analysis within the health care field, understanding the importance of data is essential.One of the primary uses of numerical data in health care is in patient care. By collecting and analyzing data on patients' medical history, symptoms, and treatment plans, healthcare professionals can gain insights into patterns and trends. This information enables them to make accurate diagnoses, develop personalized treatment plans, and monitor the progress of patients over time. Moreover, data analysis helps identify risk factors and predict potential health issues, allowing for proactive interventions and preventive measures.Another critical aspect of data in health care is research and development. Clinical trials and studies collect vast amounts of numerical data to test the efficacy and safety of new treatments, medications, and medical devices. This data is analyzed to determine the effectiveness of interventions, identify side effects, and provide evidence for regulatory approvals. Through rigorous data analysis, researchers can contribute to medical advancements, shaping the future of health care.Furthermore, data plays a crucial role in health care management. Administrators and policymakers rely on data to evaluate the performance of healthcare facilities, identify areas for improvement, and allocate resources effectively. By analyzing data on patient satisfaction, wait times, and cost-effectiveness, administrators can enhance the quality of care, streamline operations, and optimize resource allocation. This not only ensures better patient outcomes but also contributes to the overall sustainability of the health care system.

Leverage BigQuery to understand and prepare your data to ensure that it's accurate, reliable, and ready for analysis and modelingKey FeaturesUse mock datasets to explore data with the BigQuery web UI, bq CLI, and BigQuery API in the Cloud consoleMaster optimization techniques for storage and query performance in BigQueryEngage with case studies on data exploration and preparation for advertising, transportation, and customer support dataPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges.The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem.The book doesn't merely offer theoretical insights; it's a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You'll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems.By the end of this book, you'll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.What you will learnAssess the quality of a dataset and learn best practices for data cleansingPrepare data for analysis, visualization, and machine learningExplore approaches to data visualization in BigQueryApply acquired knowledge to real-life scenarios and design patternsSet up and organize BigQuery resourcesUse SQL and other tools to navigate datasetsImplement best practices to query BigQuery datasetsGain proficiency in using data preparation tools, techniques, and strategiesWho this book is forThis book is for data analysts seeking to enhance their data exploration and preparation skills using BigQuery. It guides anyone using BigQuery as a data warehouse to extract business insights from large datasets. A basic understanding of SQL, reporting, data modeling, and transformations will assist with understanding the topics covered in this book.Table of ContentsIntroducing BigQuery and Its ComponentsBigQuery Organization and DesignExploring Data in BigQueryLoading and Transforming DataQuerying BigQuery DataExploring Data with NotebooksFurther Exploring and Visualizing DataAn Overview of Data Preparation ToolsCleansing and Transforming DataBest Practices for Data Preparation, Optimization, and Cost ControlHands-On Exercise - Analyzing Advertising DataHands-On Exercise Analyzing Transportation DataHands-On Exercise - Analyzing Customer Support DataSummary and Future Directions

Business Mentalcoaching kombiniert die besten Aspekte des Spitzensports und der tiefen Persönlichkeitsarbeit, um im Geschäftsumfeld maximale Wirkung zu erzielen. Erfolgskonzepte aus zwei bewährten und erfolgreichen Welten werden fusioniert und speziell für die Rahmenbedingungen im Businessbereich kombiniert. Mentale Arbeit hat im Spitzensport unverzichtbar an Bedeutung gewonnen und auch in anderen Bereichen erkennt man zunehmend die Wirkungskraft von gezielten mentalen Übungen. Heidi Haberl-Glantschnig und Katharina Janauschek haben eine Kombination aus Coaching und Mentaltraining im Business erfolgreich angewandt, um auf effektive und leicht zugängliche Weise bewusste/rationale, wie auch die unbewussten/emotionalen Anteile der Menschen anzusprechen und die gewonnenen Erkenntnisse in den strategischen Business-Alltag zu integrieren. Der Schlüssel zum Erfolg liegt dabei in der Anwendung von effektiven Methoden, die im schnell getakteten Business-Kontext niederschwellig und effizient umsetzbar sind. Die Autorinnen zeigen auf, wie Business Mentalcoaching auf einfache Art und Weise im Arbeitsalltag Platz finden kann, z.B. für Führungskräfte als effizientes Führungstool, für Personaler als Unterstützung in der Personalentwicklung oder für Mitarbeitende in der Zielorientierung von Projekten.

Das Buch bietet einen praktischen Leitfaden, wie eine grundlegende strategische und strukturelle Neuausrichtung eines Unternehmens erfolgreich durchgeführt werden kann. Da jedes Unternehmen in seinem Kern und seiner Struktur einzigartig ist, handelt es sich nicht um eine Checkliste, die Schritt für Schritt abgearbeitet wird. Das Fachbuch gibt vielmehr einen Überblick zu konkreten Handlungsfeldern, die Unternehmen für ihren individuellen Bedarf anpassen sollten. Der Autor beschreibt einen aus der industriellen Praxis basierenden Ansatz für eine Unternehmensrestrukturierung bestehend aus sieben Bausteinen (7P): Plan, Personen, Partitionierung, Prozesse, Plattformen, Portfolio und Performance. Erfolgsentscheidend für eine gelingende Restruktruktierung ist die situative und individuelle Ausgestaltung der jeweiligen Bausteine, sodass ein Unternehmen den notwendigen Wandel rechtzeitig, rasch und effizient vollziehen kann. Zahlreiche Beispiele aus der betriebswirtschaftlichen Praxis vermitteln dem Leser Impulse, um die Bausteine aufzugreifen und zu adaptieren sowie von den Fehlern anderer zu lernen.

Angriffe aus den eigenen Reihen durch sogenannte Innentäter sind für Unternehmen nur schwer zu erkennen. Unter Innentätern versteht man Personen, die in Unternehmen oder Organisationen vorsätzlich dolose Handlungen vornehmen. Ihnen fällt es aufgrund ihres Wissens über Interna leichter, etablierte Schutzmaßnahmen zu überwinden. Unternehmensinhaber trifft im Rahmen des mehrdeutigen Begriffs unternehmerischer Compliance auch eine Pflicht, in ihrem Betrieb für regelgetreues Verhalten der Beschäftigten zu sorgen. Es besteht insofern ein Spannungsverhältnis zwischen dem Recht auf informationelle Selbstbestimmung des Beschäftigten, welches durch das Datenschutzrecht geschützt wird, und der Pflicht des Arbeitgebers, in seinem Unternehmen für rechtskonformes Verhalten zu sorgen. Gegenstand des Buchs ist daher die Frage, welche Konsequenzen die Datenschutz-Grundverordnung sowie die Gesetzgebung auf nationalstaatlicher Ebene für den Beschäftigtendatenschutz im Allgemeinen sowie für die Umsetzung der neuen Anforderungen bei unternehmensinternen Compliance-Maßnahmen hat.

Spätestens seit den massiven Desinformationsangriffen auf westliche Impfstoffhersteller während der Coronapandemie ist klar: Gezielt gestreute ¿Fake News¿ haben sich vom politischen in den wirtschaftlichen Raum ausgebreitet und Unternehmen werden sich zunehmend bewusst, dass auch sie zur Zielscheibe werden können. Die Folgen eines Desinformationsangriffs können gravierend sein: Wertverluste, Reputationsschäden sowie verunsicherte Mitarbeitende, Geschäftspartner und Investoren. Die modernen Kommunikationskrieger verfügen im Internet und in den sozialen Medien über ein mächtiges, breit gefächertes und relativ kostengünstiges Arsenal, mit dem sich Desinformationen in minutenschnelle effektiv verbreiten lassen, sodass Unternehmen kaum Zeit bleibt, sich wirksam dagegen zu wehren. Uwe Wolff beleuchtet die neue Situation, in der sich Unternehmen befinden, gibt Auskunft zur Abwehr von Desinformationsangriffen und zu deren zukünftiger Entwicklung. Dazu interviewte er unter anderem Medienanwälte und Kriminologen, Unternehmens- und IT-Berater, einen Darknet-Spezialisten, sowie einen Medien-Prankster und den ehemaligen Vorsitzenden der deutschen Wikimedia.

This book presents a comprehensive overview of Natural Language Interfaces to Databases (NLIDBs), an indispensable tool in the ever-expanding realm of data-driven exploration and decision making. After first demonstrating the importance of the field using an interactive ChatGPT session, the book explores the remarkable progress and general challenges faced with real-world deployment of NLIDBs. It goes on to provide readers with a holistic understanding of the intricate anatomy, essential components, and mechanisms underlying NLIDBs and how to build them. Key concepts in representing, querying, and processing structured data as well as approaches for optimizing user queries are established for the reader before their application in NLIDBs is explored. The book discusses text to data through early relevant work on semantic parsing and meaning representation before turning to cutting-edge advancements in how NLIDBs are empowered to comprehend and interpret human languages. Various evaluation methodologies, metrics, datasets and benchmarks that play a pivotal role in assessing the effectiveness of mapping natural language queries to formal queries in a database and the overall performance of a system are explored. The book then covers data to text, where formal representations of structured data are transformed into coherent and contextually relevant human-readable narratives. It closes with an exploration of the challenges and opportunities related to interactivity and its corresponding techniques for each dimension, such as instances of conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language. This book provides a balanced mixture of theoretical insights, practical knowledge, and real-world applications that will be an invaluable resource for researchers, practitioners, and students eager to explore the fundamental concepts of NLIDBs.

This edited book presents scientific results of the 12th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2013) which was held on June 16-20, 2013 in Toki Messe, Niigata, Japan. The aim of this conference was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas, research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve themThe conference organizers selected the best 20 papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rigorous rounds of review.

This book presents advances in security assurance for cyber-physical systems (CPS) and report on new machine learning (ML) and artificial intelligence (AI) approaches and technologies developed by the research community and the industry to address the challenges faced by this emerging field.Cyber-physical systems bridge the divide between cyber and physical-mechanical systems by combining seamlessly software systems, sensors, and actuators connected over computer networks. Through these sensors, data about the physical world can be captured and used for smart autonomous decision-making.This book introduces fundamental AI/ML principles and concepts applied in developing secure and trustworthy CPS, disseminates recent research and development efforts in this fascinating area, and presents relevant case studies, examples, and datasets. We believe that it is a valuable reference for students, instructors, researchers, industry practitioners, and related government agencies staff.

Big Data technologies have the potential to revolutionize the agriculture sector, in particular food safety and quality practices. This book is designed to provide a foundational understanding of various applications of Big Data in Food Safety. Big Data requires the use of sophisticated approaches for cleaning, processing and extracting useful information to improve decision-making. The contributed volume reviews some of these approaches and algorithms in the context of real-world food safety  studies.  Food safety and quality related data are being generated in large volumes and from a variety of sources such as farms, processors, retailers, government organizations, and other industries. The editors have included examples of how big data can be used in the fields of bacteriology, virology and mycology to improve food safety.  Additional chapters detail how the big data sources are aggregated and used in food safety and quality areas such as food spoilage and quality deterioration along the supply chain, food supply chain traceability, as well as policy and regulations. The volume also contains solutions to address standardization, data interoperability, and other data governance and data related technical challenges. Furthermore, this volume discusses how the application of machine-learning has successfully improved the speed and/or accuracy of many processes in the food supply chain, and also discusses some of the inherent challenges. Included in this volume as well is a practical example of the digital transformation that happened in Dubai, with a particular emphasis on how data is enabling better decision-making in food safety. To complete this volume, researchers discuss how although big data is and will continue to be a major disruptor in the area of food safety, it also raises some important questions with regards to issues such as security/privacy, data control and data governance, all of which must be carefully considered by governments and law makers.

- A comprehensive overview of the various fields of application of data science and artificial intelligence.- Case studies from practice to make the described concepts tangible.- Practical examples to help you carry out simple data analysis projects.- BONUS in print edition: E-Book insideData Science, Big Data, Artificial Intelligence and Generative AI are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them.Using exercises and real-world examples, it will show you how to apply data science methods, build data platforms, and deploy data- and ML-driven projects to production. It will help you understand - and explain to various stakeholders - how to generate value from such endeavors. Along the way, it will bring essential data science concepts to life, including statistics, mathematics, and machine learning fundamentals, and explore crucial topics like critical thinking, legal and ethical considerations, and building high-performing data teams.Readers of all levels of data familiarity - from aspiring data scientists to expert engineers to data leaders - will ultimately learn: how can an organization become more data-driven, what challenges might it face, and how can they as individuals help make that journey a success.The team of authors consists of data professionals from business and academia, including data scientists, engineers, business leaders and legal experts. All are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and machine learning, and raising awareness of the opportunities and potential risks of these technologies.WHAT'S INSIDE //- Critical Thinking and Data Culture: How evidence driven decision making is the base for effective AI.- Machine Learning Fundamentals: Foundations of mathematics, statistics, and ML algorithms and architectures- Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, for real world applications.- Foundation Models and Generative AI: Understand the strengths and challenges of generative models for text, images, video, and more.- ML and AI in Production: Turning experimentation into a working data science product.- Presenting your Results: Essential presentation techniques for data scientists.

This book covers the key principles, best practices, and practical techniques for designing and implementing visually compelling dashboards. It explores the various stages of the dashboard development process, from understanding user needs and defining goals, to selecting appropriate visual encodings, designing effective layouts, and employing interactive elements. It also addresses the critical aspect of data storytelling, examining how narratives and context can be woven into dashboards to deliver impactful insights and engage audiences.Visual Analytics for Dashboards is designed to cater to a wide range of readers, from beginners looking to grasp the fundamentals of visual analytics, to seasoned professionals seeking to enhance their dashboard design skills. For different types of readers, such as a data analyst, BI professional, data scientist, or simply someone interested in data visualization, this book aims to equip them with the knowledge and tools necessary to create impactful dashboards.What yoüll learnThe principles of data visualizationHow to create effective dashboardsMeet all the requirements for visual analytics/data visualization/dashboard coursesDeepen understanding of data presentation and analysisHow to use different kinds of tools for data analysis, such as scorecards and key performance indicators Who This Book Is ForBusiness analysts, data analysts, BI professionals, end-users, executives, developers, as well as students in dashboards, data visualizations, and visual analytics courses.

Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovationsKey FeaturesLearn to build robust ML pipeline solutions for databricks transitionMaster commonly available features like AutoML and MLflowLeverage data governance and model deployment using MLflow model registryPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform.You'll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you'll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You'll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows.By the end of this book, you'll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learnTransition smoothly from DIY setups to databricksMaster AutoML for quick ML experiment setupAutomate model retraining and deploymentLeverage databricks feature store for data prepUse MLflow for effective experiment trackingGain practical insights for scalable ML solutionsFind out how to handle model drifts in production environmentsWho this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.Table of ContentsML Process and ChallengesOverview of ML on DatabricksUtilizing Feature Store Understanding MLflow ComponentsCreate a Baseline Model for Bank Customer Churn Prediction Using AutoMLModel Versioning and WebhooksModel Deployment ApproachesAutomating ML Workflows Using the Databricks JobsModel Drift Detection for Our Churn Prediction Model and RetrainingCI/CD to Automate Model Retraining and Re-Deployment.

Data modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market. This practical book serves as your fast-forward button for data modeling with Power BI, Analysis Services tabular, and SQL databases. It serves as a starting point for data modeling, as well as a handy refresher. Author Markus Ehrenmueller-Jensen, founder of Savory Data, shows you the basic concepts of Power BI's semantic model with hands-on examples in DAX, Power Query, and T-SQL. If you're looking to build a data warehouse layer, chapters with T-SQL examples will get you started. You'll begin with simple steps and gradually solve more complex problems. This book shows you how to: Normalize and denormalize with DAX, Power Query, and T-SQL Apply best practices for calculations, flags and indicators, time and date, role-playing dimensions and slowly changing dimensions Solve challenges such as binning, budget, localized models, composite models, and key value with DAX, Power Query, and T-SQL Discover and tackle performance issues by applying solutions in DAX, Power Query, and T-SQL Work with tables, relations, set operations, normal forms, dimensional modeling, and ETL

"Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, and how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs. With this book, you'll: gain a working understanding of several data architectures; learn the strengths and weakness of each approach; distinguish data architecture theory from the reality; pick the best architecture for your use case; understand the differences between data warehouses and data lakes; learn common data architecture concepts to help you build better solutions; explore the historical evolution and characteristics of data architectures; learn essentials of running an architecture design session, team organization, and project success factors."--

If you use Linux in your day-to-day work, then Linux Pocket Guide is the perfect on-the-job reference. This thoroughly updated 20th anniversary edition explains more than 200 Linux commands, including new commands for file handling, package management, version control, file format conversions, and more. In this concise guide, author Daniel Barrett provides the most useful Linux commands grouped by functionality. Whether you're a novice or an experienced user, this practical book is an ideal reference for the most important Linux commands. You'll learn: Essential concepts--commands, shells, users, and the filesystem File commands-creating, organizing, manipulating, and processing files of all kinds Sysadmin basics-superusers, processes, user management, and software installation Filesystem maintenance-disks, RAID, logical volumes, backups, and more Networking commands-working with hosts, network connections, email, and the web Getting stuff done-everything from math to version control to graphics and audio

Text mining has emerged as one of the most important data processing activities over the last few decades. While it makes the life of millions of everyday users of digital plat- forms and applications much easier, it is a domain that also challenges researchers in numerous ways. The challenges are many fold - ranging from the volume of the data that needs to be processed, storage issues, language identi¿cation challenges and many more. The work in this thesis focuses on one particular aspect of text mining e.g. Key- word identi¿cation for a document. While this may seem to be quite a trivial activity for short passages, it is quite dif¿cult to successfully identify keywords for extremely long text documents. Doing so using an automated systems only adds to the challenge. Everyone in today's world understands the importance of data. In the context of business, data is used to analyze market trends or can be used to understand customer needs. It also helps to understand the user's perspectives and choices. There are var-ious ways that data plays a crucial role in our everyday lives. Most businesses would be bound to fail if they could not comprehend the data that was available. This data could vary from stock indices, to customer feedback, to worker sentiments and numerous other insights. Analyzing data also helps in advertisement noti¿cations or to suggest a piece of relevant information to the user. It also helps to understand the likes and dislikes of a user. It can make for a world with a better user experience in terms of an individuals needs, e.g., if a user is more interested in cricket, we can provide targeted insight to the user about cricket. A customized user experience for a user is more at- tractive than a bland user experience which is homogeneous for everyone. Everyone's needs are different from others as everyone has different perspectives and opinions. We offered examples of keyword extraction, the challenges involved and the major issues faced by designers of keyword extraction algorithms. Finally about some common application areas where keyword extraction is being used in real life scenarios. Attracting users and providing them with better services through relevant data also helps the system to understand the users' needs. A user consciously or unknowingly provides his information for use in business or expresses his views on various platforms. If a user expresses some political opinions, it helps us to tailor his experience better the next time he uses the system.

This book constitutes the refereed proceedings of the 5th International Conference on Science of Cyber Security, SciSec 2023, held in Melbourne, VIC, Australia, during July 11¿14, 2023. The 21 full papers presented together with 6 short papers were carefully reviewed and selected from 60 submissions. The papers are organized in the topical sections named: ¿ACDroid: Detecting Collusion Applications on Smart Devices; Almost Injective and Invertible Encodings for Jacobi Quartic Curves; Decompilation Based Deep Binary-Source Function Matching.

Dieser Quick Guide gibt einen Überblick über Compliance, ESG und Investigations in Emerging Markets im Kontext wachsender Anforderungen wie des deutschen Lieferkettensorgfaltspflichtengesetzes und EU-Verordnungen. Deutsche Unternehmen sind durch neue Regelungen, besonders in Emerging Markets, mit verstärkten Sorgfalts-, Dokumentations- und Berichterstattungspflichten konfrontiert. Dieser Leitfaden beleuchtet diese Herausforderungen und zeigt, wie On-Site Audits effizient durchgeführt werden können.Der Inhalt:EinführungAnknüpfungspunkte: Due Diligence, Supply Chain Compliance, Nachhaltigkeitsberichterstattung, KorruptionsvermeidungBetroffene Kreise: Unternehmen, Tochtergesellschaften, Zulieferer, DritteOrganisation von On-Site AuditsZusammenfassung und AusblickDie ZielgruppenCEOs, CFOs, Rechtsabteilungsleiter, Compliance-Beauftragte, Beschaffungsmitarbeiter, Rechtsanwälte

Vermittelt Ihnen Grundkenntnisse der systemischen Organisationstheorie für eine wirksame Begleitung von organisationalen Veränderungsprozessen.Mit Konzepten, die auf Menschen und ihre Psychodynamiken fokussieren, kommt man bei der Arbeit mit Organisationen nicht weit. Statt über die Grenzen der Positiven Psychologie zu lamentieren, verknüpft dieses Buch den potenzialorientierten Ansatz des Positive Organizational Scholarship (POS) mit Grundlagen der systemischen Organisationstheorie, um Transformationen in Organisationen wirksam zu begleiten. Während sich mit ¿Positive Leadership¿ bereits vor Jahren ein radikal neues Verständnis von Führung etabliert hat, definiert ¿Positive Organizing¿ den Prozess des Organisierens neu. Positive Organizing wird als Prozessmodell vorgestellt, das Berater:innen und Führungskräfte einsetzen können, um Organisationen in ihrer Fähigkeit zu stärken, selbstorganisiert und potenzialorientiert Veränderungen zu bewirken. Zu den Zielgruppen:Führungskräfte, Berater:innen, Coaches, und alle, die Veränderungen in Organisationen begleiten.

¿Ist unser Unternehmen optimal für zukünftige Entwicklungen aufgestellt?¿, ¿Gelingt es uns, die wesentlichen Transformationen im Umfeld zu erkennen und mitzuprägen?¿, ¿Sind wir fähig, Innovationen für eine noch unbekannte Welt zu entwickeln?¿ ¿ diese und ähnliche Fragen beschäftigen Führungskräfte, da sie für die Zukunftsfähigkeit einer Organisation von hoher Relevanz sind.Der Blick in die Zukunft ist ungewiss, herausfordernd und für jedes Unternehmen höchst individuell. Umso offener, positiver und konstruktiver Menschen und Organisationen auf das Morgen blicken, desto größer unsere Handlungsspielräume. Jedes Unternehmen, das den Anspruch stellt, die Zukunft aktiv mitgestalten zu wollen, braucht einen weitblickenden Prozess, der nutzerorientierte Innovationsmodelle um die Entwicklung von Zukunftsbildern erweitert.Das vorliegende Buch bietet einen neuen, zukunftsorientierten Innovationsansatz, der das klassische nutzerorientierte Innovationsmodell fortentwickelt und um die Zukunftsperspektive erweitert. Die Autorin erläutert anschaulich, wie die Auseinandersetzung mit wünschenswerten Zukünften die Innovationsarbeit in Unternehmen wesentlich bereichern kann und stellt ein Modell vor, das bekannte Innovationsprozesse um das Explorieren von Trends, das Formulieren von Visionen und das proaktive Entwickeln von Strategien ergänzt.Ausgewählte Fallbeispiele von Unternehmen zeigen, wie individuell und vielschichtig die Gestaltung der Unternehmenszukunft in der Praxis aussehen kann. Sie dienen damit als Inspiration für den eigenen Umgang in der Innovationsarbeit. Mit Beiträgen von:Barbara BoucekThomas FrauscherMarkus KretschmerChristopher LindingerSandra MagerMartina MaraAndrea MassimianiElisabeth SpitzenbergerPatricia Wiendl-Stark

Dieses neuartige Lehrbuch richtet sich an Studierende, die entweder einen Einstieg in den Bereich der Grundrechte suchen oder aber die Grundrechte zwecks Vorbereitung auf die Ubungen bzw. das Staatsexamen wiederholen mochten. Das Werk ist konsequent auf die Anforderungen zugeschnitten, mit denen Studierende in Klausuren und Hausarbeiten konfrontiert werden. Alle Grundrechte sowie die prufungsrelevanten Grundzuge der Verfassungsbeschwerde werden ausgehend vom Verfassungstext systematisch erschlossen. Die allgemeinen Grundrechtslehren, die in ihrer Abstraktheit gerade fur den Einsteiger haufig nur schwer verstandlich sind, werden nicht "e;vor die Klammer"e; gezogen, sondern am Beispiel einzelner Grundrechte behandelt. Zum besseren Verstandnis gibt das Buch auerdem die zentralen Entscheidungen des BVerfG in den relevanten Auszugen wieder. Beispielsfalle, deren Losungen uber das Internet bereitgestellt werden, sowie Zusammenstellungen typischer Klausurprobleme runden das Lehrbuch ab. 

This book presents a foray into the fascinating process of risk management, beginning from classical methods and approaches to understanding risk all the way into cutting-age thinking. Risk management by necessity must lie at the heart of governing our ever more complex digital societies. New phenomena and activities necessitate a new look at how individuals, firms, and states manage the uncertainty they must operate in. Initial chapters provide an introduction to traditional methods and show how they can be built upon to better understand the workings of the modern economy. Later chapters review digital activities and assets like cryptocurrencies showing how such emergent risks can be conceptualized better. Network theory figures prominently and the book demonstrates how it can be used to gauge the risk in the digital sectors of the economy. Predicting the unpredictable black swan events is also discussed in view of a wider adoption of economic simulations. The journey concludes by looking at how individuals perceive risk and make decisions as they operate in a virtual social network. This book interests the academic audience, but it also features insights and novel research results that are relevant for practitioners and policymakers. 

This book is very beneficial for early researchers/faculty who want to work in deep learning and machine learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies studies' image and data classifications. The early start-up can use it to work with product or prototype design requirement analysis and its design and development.

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications. 

Vor dem Hintergrund des digitalen Wandels haben sich auch die Markt- und Wettbewerbsbedingungen der Beratungsunternehmen verändert. Eine Branche, deren Wurzeln zumeist bei Technikern und Tüftlern zu suchen sind, steht vor der Herausforderung, unter den veränderten Rahmenbedingungen erfolgversprechende und schlüssige Marketing-Konzepte zu entwickeln. Das Buch verfolgt das Ziel, ein Vorgehensmodell für den Vermarktungsprozess von Beratungsleistungen zu entwerfen. Es liefert eine phasenbezogene Darstellung der Vermarktung, die dem Management von Unternehmensberatungen Ansatzpunkte bietet, um die einzelnen Elemente der Marketing-Gleichung zu optimieren.