Data mining

This book constitutes revised papers from the International Workshops held at the 20th International Conference on Business Process Management, BPM 2022, in Münster, Germany, during September 11-15, 2022. Papers from the following workshops are included:· 6th International Workshop on Artificial Intelligence for Business Process Management (AI4BPM 2022)· 6th International Workshop on Business Processes Meet Internet-of-Things (BP-Meet-IoT 2022)· 18th International Workshop on Business Process Intelligence (BPI 2022)· 2nd International Workshop on Business Process Management and Routine Dynamics (BPM&RD 2022)· 14th International Workshop on Social and Human Aspects of Business Process Management (BPMS2 2022) · 1st International Workshop on Data-Driven Business Process Optimization (BPO 2022) · 10th International Workshop on DEClarative, DECision and Hybrid approaches to processes (DEC2H 2022) · 1st International Workshop on Natural Language Processing for Business Process Management (NLP4BPM 2022) Each of the eight workshops focused on particular aspects of business process management. Overall, after a thorough review process, there were 23 full and 3 short papers selected from a total of 51 submissions. Only one of the short papers is included in the proceedings.

Are you suffering from Data Presentation Zombification?Billions of dollars and thousands of hours are lost every year during ineffective business meetings worldwide. Data practitioners painstakingly present their valuable analytical insights, only to fall flat, inspiring more yawns than yeses.In Present Beyond Measure: Design, Visualize, and Deliver Data Stories That Inspire Action, Lea Pica provides a 4-phase, step-by-step blueprint for planning, designing, visualizing, and delivering compelling data storytelling in business presentations. Following her blueprint, you will learn how to use neuroscience and cinematic storytelling techniques to galvanize your stakeholders into action.By the final page, you'll know exactly how to:* Choose the data that matters most to your decision-makers* Speak to different stakeholder audience personality types (even the most challenging)* Infuse your data presentation with a persuasive narrative storyline* Craft strategic recommendations that get approved and implemented* Design simple, stunning slides that communicate without confusing* Transmit your data story with best-practice data visualization techniques* Avoid the most common data visualization violations and charting pitfalls* Prepare for and deliver your presentation like a professional speaker* Navigate challenging meeting conversations and logistics with easeWhether you work with little or big data, this book will show you how to prevent presentation zombies and inspire the action and credibility you and your organization deserve.

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.

This book constitutes the refereed proceedings of the 28th China Conference on Information Retrieval, CCIR 2022, held in Chongqing, China, in September 2022. Information retrieval aims to meet the demand of human on the Internet to obtain information quickly and accurately. The 8 full papers presented were carefully reviewed and selected from numerous submissions. The papers provide a wide range of research results in information retrieval area.

This book highlights an innovative approach for extracting terminological cores from subject domain-bounded collections of professional texts. The approach is based on exploiting the phenomenon of terminological saturation. The book presents the formal framework for the method of detecting and measuring terminological saturation as a successive approximation process. It further offers the suite of the algorithms that implement the method in the software and comprehensively evaluates all the aspects of the method and possible input configurations in the experiments on synthetic and real collections of texts in several subject domains. The book demonstrates the use of the developed method and software pipeline in industrial and academic use cases. It also outlines the potential benefits of the method for the adoption in industry.

This book includes high-quality papers presented at the Second International Conference on Data Science and Management (ICDSM 2021), organized by the Gandhi Institute for Education and Technology, Bhubaneswar, from 19 to 20 February 2021. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands.

This book constitutes the proceedings of the 10th International Conference on Big Data Analytics, BDA 2022, which took place in Hyderabad, India, in December 2022.The 7 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 36 submissions. The book also contains 4 keynote talks in full-paper length. The papers are organized in the following topical sections: Big Data Analytics: Vision and Perspectives; Data Science: Architectures; Data Science: Applications; Graph Analytics; Pattern Mining; Predictive Analytics in Agriculture.

This book provides a comprehensive overview of core concepts and technological foundations for continuous engineering of Web streams. It presents various systems and applications and includes real-world examples. Last not least, it introduces the readers to RSP4J, a novel open-source project that aims to gather community efforts in software engineering and empirical research.The book starts with an introductory chapter that positions the work by explaining what motivates the design of specific techniques for processing data streams using Web technologies. Chapter 2 briefly summarizes the necessary background concepts and models needed to understand the remaining content of the book. Subsequently, chapter 3 focuses on processing RDF streams, taming data velocity in an open environment characterized by high data variety. It introduces query answering algorithms with RSP-QL and analytics functions over streaming data. Chapter 4 presents the life cycle of streaming linked data, it focuses on publishing streams on the Web as a prerequisite aspect to make data findable and accessible for applications. Chapter 5 touches on the problems of benchmarks and systems that analyze Web streams to foster technological progress. It surveys existing benchmarks and introduces guidelines that may support new practitioners in approaching the issue of continuous analytics. Finally, chapter 6 presents a list of examples and exercises that will help the reader to approach the area, get used to its practices and become confident in its technological possibilities.Overall, this book is mainly written for graduate students and researchers in Web and stream data management. It collects research results and will guide the next generation of researchers and practitioners.

This volume presents techniques and theories drawn from mathematics, statistics, computer science, and information science to analyze problems in business, economics, finance, insurance, and related fields. The authors present proposals for solutions to common problems in related fields. To this end, they are showing the use of mathematical, statistical, and actuarial modeling, and concepts from data science to construct and apply appropriate models with real-life data, and employ the design and implementation of computer algorithms to evaluate decision-making processes. This book is unique as it associates data science - data-scientists coming from different backgrounds - with some basic and advanced concepts and tools used in econometrics, operational research, and actuarial sciences. It, therefore, is a must-read for scholars, students, and practitioners interested in a better understanding of the techniques and theories of these fields.

This book constitutes the proceedings of the 14th IFIP WG 8.5 International Conference on Electronic Participation, ePart 2022, held in Linköping, Sweden, during September 6¿8, 2022, in conjunction with IFIP WG 8.5 Electronic Government (EGOV 2022), and the Conference for E-Democracy and Open Government Conference (CeDEM 2022).The 12 full papers presented were carefully reviewed and selected from 26 submissions. The papers are clustered under the following topical sections: E-democracy and e-participation; ICT & sustainability; digital and social media; legal informatics; and digital society.

The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume XXIII in the series is a continuation of a number of research streams that have grown out of the seminal work of Zdzislaw Pawlak during the first decade of the 21st century.

The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.

This book constitutes the proceedings of the 19th International Conference on Web Information Systems and Applications, WISA 2022, held in Dalian, China, in September 2022.The 45 full papers and 19 short papers presented were carefully reviewed and selected from 212 submissions. The papers are grouped in topical sections on knowledge graph, natural language processing, world wide web, machine learning, query processing and algorithm, recommendation, data privacy and security, and blockchain.

This book constitutes the refereed proceedings of the 35th Australasian Joint Conference on Artificial Intelligence, AI 2022, which took place in Perth, WA, Australia, in December 5-8, 2022. The 56 full papers included in this book were carefully reviewed and selected from 90 submissions. They were organized in topical sections as follows: Computer Vision; Deep Learning; Ethical/Explainable AI; Genetic Algorithms; Knowledge Representation and NLP; Machine Learning; Medical AI; Optimization; and Reinforcement Learning.