Data mining

Der menschliche Verstand ist evolutionär darauf ausgelegt, Abkürzungen zu nehmen, um zu überleben. Wir ziehen voreilige Schlüsse, weil unser Gehirn uns in Sicherheit wiegen will. Die meisten unserer Voreingenommenheiten wirken sich zu unseren Gunsten aus, z. B. wenn wir ein Auto, das in unsere Richtung fährt, für gefährlich halten und sofort ausweichen oder wenn wir beschließen, einen Bissen Essen, der verdorben zu sein scheint, nicht zu essen. Allerdings wirken sich inhärente Vorurteile negativ auf das Arbeitsumfeld und die Entscheidungsfindung in unseren Gemeinschaften aus. Mit der Entwicklung von Algorithmen und maschinellem Lernen wird zwar versucht, Voreingenommenheit zu beseitigen, aber schließlich werden sie doch von Menschen geschaffen und sind daher anfällig für das, was wir als algorithmische Voreingenommenheit bezeichnen.In Understand, Manage, and Prevent Algorithmic Bias (Algorithmische Voreingenommenheit verstehen, verwalten und verhindern) hilft Ihnen der Autor Tobias Baer zu verstehen, woher algorithmische Voreingenommenheit kommt, wie man sie als Geschäftsanwender oder Regulierungsbehörde handhaben kann und wie die Datenwissenschaft verhindern kann, dass Voreingenommenheit in statistische Algorithmen einfließt. Baer befasst sich fachkundig mit einigen der mehr als 100 Arten natürlicher Verzerrungen wie Confirmation Bias, Stability Bias, Pattern Recognition Bias und vielen anderen. Algorithmische Voreingenommenheit spiegelt diese menschlichen Tendenzen wider und hat ihren Ursprung in ihnen.Während sich die meisten Schriften über algorithmische Voreingenommenheit auf die Gefahren konzentrieren, weist der Kern dieses positiven, unterhaltsamen Buches auf einen Weg hin, auf dem Voreingenommenheit in Schach gehalten und sogar beseitigt wird. Sie erhalten Managementtechniken, um unvoreingenommene Algorithmen zu entwickeln, die Fähigkeit, Voreingenommenheit schneller zu erkennen, und das Wissen, um unvoreingenommene Daten zu erstellen. Algorithmic Bias verstehen, verwalten und verhindern ist ein innovatives, zeitgemäßes und wichtiges Buch, das in Ihr Regal gehört. Ganz gleich, ob Sie eine erfahrene Führungskraft, ein Datenwissenschaftler oder einfach nur ein Enthusiast sind - jetzt ist ein entscheidender Zeitpunkt, um sich über die größeren soziologischen Auswirkungen von Verzerrungen im digitalen Zeitalter zu informieren.Dieses Buch stellt die Übersetzung einer englischsprachigen Originalausgabe dar. Die Übersetzung wurde mit Hilfe von künstlicher Intelligenz erstellt (maschinelle Übersetzung mit DeepL.com). Eine anschließende manuelle Überarbeitung erfolgte vor allem nach inhaltlichen Gesichtspunkten, so dass das Buch stilistisch von einer herkömmlichen Übersetzung abweichen kann.

Künstliche Intelligenz berührt fast jeden Teil Ihres Alltags. Auch wenn Sie auf den ersten Blick annehmen, dass Technologien wie intelligente Lautsprecher und digitale Assistenten das Ausmaß dieser Technologie darstellen, hat sich KI in der Tat schnell zu einer Allzwecktechnologie entwickelt, die in Branchen wie dem Transportwesen, dem Gesundheitswesen, den Finanzdienstleistungen und vielen mehr Einzug gehalten hat. In unserer modernen Zeit ist ein Verständnis von KI und ihren Möglichkeiten für Ihr Unternehmen unerlässlich für Wachstum und Erfolg.Grundlagen der Künstlichen Intelligenz ist da, um Ihnen ein grundlegendes, zeitgemäßes Verständnis von KI und ihren Auswirkungen zu vermitteln. Der Autor Tom Taulli bietet eine ansprechende, nicht-technische Einführung in wichtige Konzepte wie maschinelles Lernen, Deep Learning, natürliche Sprachverarbeitung (NLP), Robotik und mehr. Taulli führt Sie nicht nur durch reale Fallstudien und praktische Implementierungsschritte, sondern nutzt sein Fachwissen auch, um auf die größeren Fragen einzugehen, die KI umgeben. Dazu gehören gesellschaftliche Trends, ethische Fragen und die zukünftigen Auswirkungen von KI auf Regierungen, Unternehmensstrukturen und das tägliche Leben.Google, Amazon, Facebook und ähnliche Tech-Giganten sind bei weitem nicht die einzigen Unternehmen, auf die künstliche Intelligenz eine unglaublich bedeutende Auswirkung hat - und weiterhin haben wird. KI ist die Gegenwart und die Zukunft Ihres Unternehmens und Ihres Privatlebens. Die Vertiefung Ihrer Kenntnisse in diesem Bereich ist von unschätzbarem Wert für Ihre Vorbereitung auf die Zukunft der Technik, und Grundlagen der Künstlichen Intelligenz ist der unverzichtbare Leitfaden, nach dem Sie gesucht haben.Was Sie lernen werdenStudium der Grundprinzipien für KI-Ansätze wie maschinelles Lernen, Deep Learning und NLP (Natural Language Processing)Entdecken Sie die besten Praktiken zur erfolgreichen Implementierung von KI anhand von Fallstudien wie Uber, Facebook, Waymo, UiPath und Stitch FixVerstehen Sie, wie KI-Funktionen für Roboter das Geschäft verbessern könnenEinsatz von Chatbots und Robotic Processing Automation (RPA) zur Kosteneinsparung und Verbesserung des KundendienstesVermeiden Sie kostspielige StolpersteineErkennen von ethischen Bedenken und anderen Risikofaktoren beim Einsatz künstlicher IntelligenzUntersuchen Sie langanhaltende Trends und wie sie sich auf Ihr Unternehmen auswirken könnenFür wen dieses Buch bestimmt istLeser ohne technischen Hintergrund, z. B. Manager, die KI verstehen und Lösungen bewerten wollen.Dieses Buch stellt die Übersetzung der englischsprachigen Originalausgabe dar. Die Übersetzung wurde mit Hilfe von künstlicher Intelligenz erstellt (maschinelle Übersetzung mit DeepL.com). Eine anschließende manuelle Überarbeitung erfolgte vor allem nach inhaltlichen Gesichtspunkten, so dass das Buch stilistisch von einer herkömmlichen Übersetzung abweichen kann.

This book constitutes the refereed proceedings of the 20th International Conference on Business Process Management, BPM 2022, which took place in Munster, Germany, in September 2022. The 22 papers included in this book were carefully reviewed and selected from 98 submissions. They were organized in topical sections as follows: task mining; design methods; process mining; process mining practice; analytics; and systems. The book also includes one keynote talk in full-paper length and 5 tutorial papers. 

This book constitutes the proceedings of the BPM Forum held at the 20th International Conference on Business Process Management, BPM 2022, which took place in Munster, Germany, in September 2022.The BPM Forum hosts innovative research which has a high potential of stimulating discussions. The papers selected for the forum are expected to showcase fresh ideas from exciting and emerging topics in BPM, even if they are not yet as mature as the regular papers at the conference.The 13 full papers included in this volume were carefully reviewed and selected from 98 submissions. The papers were organized in topical sections named: modeling and design; process mining; and predictive process monitoring.

This book explores the application of data mining and machine learning techniques in studying the activity pattern, decision-making skills, misconducts, and actions resulting in the intervention of VAR in European soccer leagues referees. The game of soccer at the elite level is characterised by intense competitions, a high level of intensity, technical, and tactical skills coupled with a long duration of play. Referees are required to officiate the game and deliver correct and indisputable decisions throughout the duration of play. The increase in the spatial and temporal task demands of the game necessitates that the referees must respond and cope with the physiological and psychological loads inherent in the game. The referees are also required to deliver an accurate decision and uphold the rules and regulations of the game during a match. These demands and attributes make the work of referees highly complex. The increasing pace and complexity of the game resulted in the introduction of the Video Assistant Referee (VAR) to assist and improve the decision-making of on-field referees. Despite the integration of VAR into the current refereeing system, the performances of the referees are yet to be error-free. Machine learning coupled with data mining techniques has shown to be vital in providing insights from a large dataset which could be used to draw important inferences that can aid decision-making for diagnostics purposes and overall performance improvement.A total of 6232 matches from 5 consecutive seasons officiated across the English Premier League, Spanish LaLiga, Italian Serie A as well as the German Bundesliga was studied. It is envisioned that the findings in this book could be useful in recognising the activity pattern of top-class referees, that is non-trivial for the stakeholders in devising strategies to further enhance the performances of referees as well as empower talent identification experts with pertinent information for mapping out future high-performance referees.

Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question.This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications.The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.

Discover key information buried in the noise of data by learning a variety of anomaly detection techniques and using the Python programming language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and outliers are and uses the Gestalt school of psychology to explain just why it is that humans are naturally great at detecting anomalies. From there, you will move into technical definitions of anomalies, moving beyond "e;I know it when I see it"e; to defining things in a way that computers can understand.The core of the book involves building a robust, deployable anomaly detection service in Python. You will start with a simple anomaly detection service, which will expand over the course of the book to include a variety of valuable anomaly detection techniques, covering descriptive statistics, clustering, and time series scenarios. Finally, you will compare your anomaly detection service head-to-head with a publicly available cloud offering and see how they perform.The anomaly detection techniques and examples in this book combine psychology, statistics, mathematics, and Python programming in a way that is easily accessible to software developers. They give you an understanding of what anomalies are and why you are naturally a gifted anomaly detector. Then, they help you to translate your human techniques into algorithms that can be used to program computers to automate the process. You'll develop your own anomaly detection service, extend it using a variety of techniques such as including clustering techniques for multivariate analysis and time series techniques for observing data over time, and compare your service head-on against a commercial service.What You Will LearnUnderstand the intuition behind anomaliesConvert your intuition into technical descriptions of anomalous dataDetect anomalies using statistical tools, such as distributions, variance and standard deviation, robust statistics, and interquartile rangeApply state-of-the-art anomaly detection techniques in the realms of clustering and time series analysisWork with common Python packages for outlier detection and time series analysis, such as scikit-learn, PyOD, and tslearnDevelop a project from the ground up which finds anomalies in data, starting with simple arrays of numeric data and expanding to include multivariate inputs and even time series dataWho This Book Is ForFor software developers with at least some familiarity with the Python programming language, and who would like to understand the science and some of the statistics behind anomaly detection techniques. Readers are not required to have any formal knowledge of statistics as the book introduces relevant concepts along the way.

Master the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business' operations. SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain.This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science will allow SMEs to understand their customer loyalty, market segmentation, sales and revenue increase etc. more clearly.  Data Science and Analytics for SMEs is particularly focused on small businesses and explores the analytics and data that can help them succeed further in their business. What You'll LearnCreate and measure the success of their analytics projectStart your business analytics consulting careerUse solutions taught in the book in practical uses cases and problems Who This Book Is ForBusiness analytics enthusiasts who are not particularly programming inclined, small business owners and data science consultants, data science and business students, and SME (small-to-medium enterprise) analysts 

This book constitutes revised selected papers of the 10th International Conference on Analysis of Images, Social Networks and Texts, AIST 2021, held in Tbilisi, Georgia, in December 2021. Due to the COVID-19 pandemic the conference was held in hybrid mode. The 17 full papers were carefully reviewed and selected from 118 submissions, out of which 92 were sent to peer review. The papers are organized in topical sections on natural language processing; computer vision; data analysis and machine learning; social network analysis; theoretical machine learning and optimisation.

This book constitutes the thoroughly refereed proceedings of the 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, held in Kitakyushu, Japan, in July 2022.The 67 full papers and 11 short papers presented were carefully reviewed and selected from 127 submissions. The IEA/AIE 2022 conference focuses on focuses on applications of applied intelligent systems to solve real-life problems in all areas including business and finance, science, engineering, industry, cyberspace, bioinformatics, automation, robotics, medicine and biomedicine, and human-machine interactions.

This book constitutes the proceedings of the 26th European Conference on Advances in Databases and Information Systems, ADBIS 2022, held in Turin, Italy, in September 2022. The 29 short papers presented were carefully reviewed and selected from 90 submissions. The selected short papers are organized in the following sections: data understanding, modeling and visualization; fairness in data processing; data management pipeline, information and process retrieval; data access optimization; data pre-processing and cleaning; data science and machine learning. Further, papers from the following workshops and satellite events are provided in the volume: DOING: 3rd Workshop on Intelligent Data - From Data to Knowledge; K-GALS: 1st Workshop on Knowledge Graphs Analysis on a Large Scale; MADEISD: 4th Workshop on Modern Approaches in Data Engineering and Information System Design; MegaData: 2nd Workshop on Advanced Data Systems Management, Engineering, and Analytics; SWODCH: 2nd Workshop on Semantic Web and Ontology Design for Cultural Heritage; Doctoral Consortium.

The first edition of the Encyclopedia of Complexity and Systems Science (ECSS, 2009) presented a comprehensive overview of granular computing (GrC) broadly divided into several categories: Granular computing from rough set theory, Granular Computing in Database Theory, Granular Computing in Social Networks, Granular Computing and Fuzzy Set Theory, Grid/Cloud Computing, as well as general issues in granular computing. In 2011, the formal theory of GrC was established, providing an adequate infrastructure to support revolutionary new approaches to computer/data science, including the challenges presented by so-called big data. For this volume of ECSS, Second Edition, many entries have been updated to capture these new developments, together with new chapters on such topics as data clustering, outliers in data mining, qualitative fuzzy sets, and information flow analysis for security applications. Granulations can be seen as a natural and ancient methodology deeply rooted in the human mind. Many daily "things" are routinely granulated into sub "things": The topography of earth is granulated into hills, plateaus, etc., space and time are granulated into infinitesimal granules, and a circle is granulated into polygons of infinitesimal sides. Such granules led to the invention of calculus, topology and non-standard analysis. Formalization of general granulation was difficult but, as shown in this volume, great progress has been made in combing discrete and continuous mathematics under one roof for a broad range of applications in data science.

This LNCS 13407 constitutes the refereed proceedings of the 24th International Conference on Information and Communications Security, ICICS 2022, held in Canterbury, UK,, in September 2022. The 34 revised full papers presented in the book were carefully selected from 150 submissionsThe papers are organized around the following topics: Cryptography, Authentication, Privacy and Anonymity, Attacks and Vulnerability Analysis, Artificial Intelligence for Detection, and Network Security and Forensics.

This book constitutes the refereed proceedings of the 13th International Conference of the CLEF Association, CLEF 2022, held in Bologna, Italy in September 2022.The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data.The 7 full papers presented together with 3 short papers in this volume were carefully reviewed and selected from 14 submissions. This year, the contributions addressed the following challenges: authorship attribution, fake news detection and news tracking, noise-detection in automatically transferred relevance judgments, impact of online education on children's conversational search behavior, analysis of multi-modal social media content, knowledge graphs for sensitivity identification, a fusion of deep learning and logic rules for sentiment analysis, medical concept normalization and domain-specific information extraction.In addition to this, the volume presents 7 "e;best of the labs"e; papers which were reviewed as full paper submissions with the same review criteria. 14 lab overview papers were accepted and represent scientific challenges based on new datasets and real world problems in multimodal and multilingual information access.

This book constitutes the proceedings of the 26th European Conference on Advances in Databases and Information Systems, ADBIS 2022, held in Turin, Italy, in September 2022.The 23 full papers presented together with 5 keynote and tutorial papers were carefully reviewed and selected from 90 submissions. The papers are organized in the following topical sections: keynote talk and tutorials; graph processing; time series and data streams; on line analytical processing; advanced querying; performance; machine learning; data science methods.

This book constitutes selected, revised and extended papers from the 13th International Conference on Computer Supported Education, CSEDU 2021, held as a virtual event in April 2021.The 27 revised full papers were carefully reviewed and selected from 143 submissions. They were organized in topical sections as follows: artificial intelligence in education; information technologies supporting learning; learning/teaching methodologies and assessment; social context and learning environments; ubiquitous learning; current topics.

PRAISE FOR FREEDOM TEACHING "As a lifelong advocate for change, Matthew Kincaid has written an indispensable guide for educators and activists alike. This book brilliantly unpacks the complexities of anti-racism in education." --SALVATORE LAROCCA, President, Global Partnerships, National Basketball Association and Chairman of the Board of Trustees, La Salle Academy "Matthew Kincaid has been a leader in freedom teaching for many years, exemplifying the principles on which he writes and refining the principles he shares. He has set a powerful blueprint, and this book serves as a reminder that freedom--and hate--begin in the classroom. Freedom Teaching helps us make the right choices with intention and love." --BRITTANY PACKNETT CUNNINGHAM, activist and host of UNDISTRACTED "Matthew Kincaid has been leading anti-racism initiatives since he was 14 years old. His passion, commitment, and dedication to this cause is illustrated in the attention to detail in this book. Freedom Teaching strikes at the core of the injustice in our education system while offering vision for a more hopeful, just and equitable future." --CONSTANCE JONES, CEO, Noble Schools "Freedom Teaching is a powerful tool to advance social justice education with K-12 schools! For educators who are committed to leveraging education as a mechanism to promote justice, liberation, and empowerment, this text richly equips you with the strategies or how. I have always been a fan of Matthew Kincaid's work, and this text is yet another manifestation of his genius! I highly recommend it, and cannot wait to leverage it as a tool to support both new and seasoned educators looking for resources to expand their ability to help craft youth who are agents of change within our world!" --KRYSTAL H. ALLEN, author of What Goes Unspoken; CEO, K. Allen Consulting "When it comes to the critical truth telling needed to shake up age-old systems and structures so they can't operate as they have in the past, Matthew Kincaid is without equal. I have been an advocate of Matthew's scholarship for many years and Freedom Teaching gives me hope that it is possible for ALL children to learn in an environment justly designed for them to flourish." --LILIAHN MAJEED, DE&I Executive and Disruptor of Deficit Narratives

This volume constitutes the refereed proceedings of the workshops held at the 33rd International Conference on Database and Expert Systems Applications, DEXA 2022, held in Vienna, Austria, in August 2022: The 6th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems (IWCFS 2022); 4th International Workshop on Machine Learning and Knowledge Graphs (MLKgraphs 2022); 2nd International Workshop on Time Ordered Data (ProTime2022); 2nd International Workshop on AI System Engineering: Math, Modelling and Software (AISys2022); 1st International Workshop on Distributed Ledgers and Related Technologies (DLRT2022); 1st International Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and Data Science (ARTE2022). The 40 papers were thoroughly reviewed and selected from 62 submissions, and discuss a range of topics including: knowledge discovery, biological data, cyber security, cyber-physical system, machine learning, knowledge graphs, information retriever, data base, and artificial intelligence.