Data warehouse

"Insights from the Algorithm: A Machine Learning Story" is an engaging and informative journey into the fascinating world of machine learning and artificial intelligence. This narrative weaves together a rich tapestry of concepts, techniques, and practical applications, offering a comprehensive understanding of this rapidly evolving field.This compelling narrative takes readers on a captivating exploration of the inner workings of algorithms, data analytics, and predictive modeling. It delves deep into the ever-expanding universe of machine learning, offering insights into its foundational principles, methodologies, and real-world applications.Throughout the pages of this book, you'll embark on a quest to unravel the secrets behind the algorithms that power recommendation systems, autonomous vehicles, and natural language processing. You'll discover how machine learning algorithms are designed to detect intricate patterns within data, enabling them to make predictions and take intelligent actions.With a keen focus on demystifying complex technical concepts, "Insights from the Algorithm" serves as a beacon for both beginners and seasoned data scientists. It elucidates key topics such as supervised and unsupervised learning, deep learning, neural networks, and the ethics of AI in an accessible and engaging manner.The narrative goes beyond mere technicalities and offers a thought-provoking exploration of the societal and ethical implications of machine learning. It discusses the responsible use of AI and the impact of algorithms on decision-making, privacy, and bias, providing a comprehensive understanding of the challenges and opportunities presented by this revolutionary technology.In "Insights from the Algorithm," you'll find a treasure trove of case studies and real-world examples that illustrate the transformative power of machine learning. From healthcare and finance to marketing and autonomous robotics, this narrative highlights how machine learning is reshaping industries and offering innovative solutions to complex problems.Join us on this captivating journey as we unveil the intricate world of machine learning and discover how algorithms are not just lines of code but powerful tools that unlock new dimensions of understanding and enable intelligent actions. This book is your gateway to the future, where data-driven insights and algorithmic intelligence redefine what's possible. Whether you're an enthusiast, a student, or a professional in the field, this narrative offers a comprehensive, accessible, and inspiring guide to the incredible world of machine learning.

This book constitutes the refereed proceedings of the 19th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2023, held in conjunction with the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023), in November 2023, in Jakarta, Indonesia. The 9 full papers and 2 short papers included in this volume were carefully reviewed and selected from 28 initial submissions. They are organized in the topical section such as machine learning, natural language processing, and intelligent systems.

This book constitutes the thoroughly refereed proceedings of the 11th Chinese National Conference of Social Media Processing, SMP 2023, held in Anhui, China, in November 2023.The 16 full papers presented were carefully reviewed and selected from 88 submissions. The papers are organized in the topical sections on knowledge representation and reasoning; knowledge acquisition and knowledge base construction; linked data, knowledge integration, and knowledge graph storage management; natural language understanding and semantic computing; knowledge graph applications; knowledge graph open resources.

This book provides a principled data-driven framework that progressively constructs, enriches, and applies taxonomies without leveraging massive human annotated data. Traditionally, people construct domain-specific taxonomies by extensive manual curations, which is time-consuming and costly. In today's information era, people are inundated with the vast amounts of text data. Despite their usefulness, people haven't yet exploited the full power of taxonomies due to the heavy curation needed for creating and maintaining them. To bridge this gap, the authors discuss automated taxonomy discovery and exploration, with an emphasis on label-efficient machine learning methods and their real-world usages. Taxonomy organizes entities and concepts in a hierarchy way. It is ubiquitous in our daily life, ranging from product taxonomies used by online retailers, topic taxonomies deployed by news outlets and social media, as well as scientific taxonomies deployed by digital libraries across various domains. When properly analyzed, these taxonomies can play a vital role for science, engineering, business intelligence, policy design, e-commerce, and more. Intuitive examples are used throughout enabling readers to grasp concepts more easily.

This book constitutes the refereed proceedings of the 8th China Conference on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence, CCKS 2023, held in Shenyang, China, during August 24¿27, 2023. The 28 full papers included in this book were carefully reviewed and selected from 106 submissions. They were organized in topical sections as follows: ¿knowledge representation and knowledge graph reasoning; knowledge acquisition and knowledge base construction; knowledge integration and knowledge graph management; natural language understanding and semantic computing; knowledge graph applications; knowledge graph open resources; and evaluations.

The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 54th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains three fully revised and extended papers and two additional extended keynotes selected from the 38th conference on Data Management - Principles, Technologies and Applications, BDA 2022. The topics cover a wide range of timely data management research topics on temporal graph management, tensor-based data mining, time-series prediction, healthcare analytics over knowledge graphs, and explanation of database query answers.

The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability.This, the 55th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains five fully revised regular papers covering a wide range of very hot topics in the fields of data driven science life science, workflows, weak signals, online social networks, root cause analysis, detected anomalies, analysis of interplanetary file systems, concept weighting in knowledge graphs,and neural networks.

This book constitutes the refereed proceedings of the 22nd International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2022, which took place in Warsaw, Poland, in September 2022; the event was sponsored by IFIP WG 5.4.The 39 full papers presented were carefully reviewed and selected from 43 submissions. They are organized in the following thematic sections: New perspectives of TRIZ; AI in systematic innovation; systematic innovations supporting IT and AI; TRIZ applications; TRIZ education and ecosystem.

Are you intrigued by the world of AI and data engineering? Do you find yourself captivated by the potential of these technologies to transform businesses and industries? If so, Data Engineering and AI for Beginners is the introductory guide you've been searching for. In today's fast evolving digital landscape where data is lifeblood, those who harness its flow hold the keys to game-changing insights. This book addresses the very challenges and questions that have been on your mind:What is Data Engineering?How Does AI Fit into the Picture?Is This for Me, a Beginner?What Tools and Technologies Are Involved?Where Does This Lead Me in the Future?Data Engineering and AI for Beginners is a resource tailored to cater to your curiosity and passion for learning, whether you're an eager student, a budding professional, or an experienced practitioner seeking to level up your skills. This book is designed to pique your interest with clear explanations of concepts, a structured learning journey that spans from fundamentals to advanced topics, and comprehensive coverage from a holistic view.You will discover in this book:The Potential of AI Applications in Data EngineeringAI-driven Data Visualisation and StorytellingDeep-dived Insights from Advanced Analytics with AI AI-powered Data Governance and ComplianceAnd Much, Much More!Ask any veteran data engineer who works with artificial intelligence, and you'll hear stories of data transformed into actionable insights, hidden patterns unveiled within mountains of data, and careers elevated by the power of innovation. AI isn't just an option; it's the compass guiding data engineers towards uncharted territories of excellence. Whether you're aiming to solve business problems or to simply demystify AI and data engineering, take the first step and let Data Engineering and AI for Beginners start you on a transformative journey of mastery.

This book constitutes the refereed proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2022, held in Valletta, Malta, during October 24¿26, 2022.The 14 full papers included in this book were carefully reviewed and selected from 127 submissions. They were organized in topical sections as follows: Knowledge Discovery and Information Retrieval; Knowledge Engineering and Ontology Development; and Knowledge Management and Information Systems

This two-volume set LNAI 13995 and LNAI 13996 constitutes the refereed proceedings of the 15th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2023, held in Phuket, Thailand, during July 24-26, 2023.The 65 full papers presented in these proceedings were carefully reviewed and selected from 224 submissions. The papers of the 2 volume-set are organized in the following topical sections: Case-Based Reasoning and Machine Comprehension; Computer Vision; Data Mining and Machine Learning; Knowledge Integration and Analysis; Speech and Text Processing; and Resource Management and Optimization.

Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scaleKey Features:Leverage supervised learning to build binary classification, multi-class classification, and regression modelsLearn to use unsupervised learning using the K-means clustering methodMaster the art of time series forecasting using Redshift MLPurchase of the print or Kindle book includes a free PDF eBookBook Description:Amazon Redshift Serverless enables organizations to run petabyte-scale cloud data warehouses quickly and in a cost-effective way, enabling data science professionals to efficiently deploy cloud data warehouses and leverage easy-to-use tools to train models and run predictions. This practical guide will help developers and data professionals working with Amazon Redshift data warehouses to put their SQL knowledge to work for training and deploying machine learning models.The book begins by helping you to explore the inner workings of Redshift Serverless as well as the foundations of data analytics and types of data machine learning. With the help of step-by-step explanations of essential concepts and practical examples, you'll then learn to build your own classification and regression models. As you advance, you'll find out how to deploy various types of machine learning projects using familiar SQL code, before delving into Redshift ML. In the concluding chapters, you'll discover best practices for implementing serverless architecture with Redshift.By the end of this book, you'll be able to configure and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale.What You Will Learn:Utilize Redshift Serverless for data ingestion, data analysis, and machine learningCreate supervised and unsupervised models and learn how to supply your own custom parametersDiscover how to use time series forecasting in your data warehouseCreate a SageMaker endpoint and use that to build a Redshift ML model for remote inferenceFind out how to operationalize machine learning in your data warehouseUse model explainability and calculate probabilities with Amazon Redshift MLWho this book is for:Data scientists and machine learning developers working with Amazon Redshift who want to explore its machine-learning capabilities will find this definitive guide helpful. A basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to make the most of this book.

This book constitutes the refereed proceedings of the 21st International Conference on Business Process Management, BPM 2023, which took place in Utrecht, The Netherlands, in September 2023. The 27 papers included in this book were carefully reviewed and selected from 151 submissions. They were organized in three main research tracks: Foundations, engineering, and management.

This book constitutes the proceedings of the 23rd International TRIZ Future Conference on Towards AI-Aided Invention and Innovation, TFC 2023, which was held in Offenburg, Germany, during September 12¿14, 2023. The event was sponsored by IFIP WG 5.4.The 43 full papers presented in this book were carefully reviewed and selected from 80 submissions. The papers are divided into the following topical sections: AI and TRIZ; sustainable development; general vision of TRIZ; TRIZ impact in society; and TRIZ case studies.

This volume LNCS-IFIP constitutes the refereed proceedings of the 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023 in Benevento, Italy, during August 28 ¿ September 1, 2023. The 18 full papers presented together were carefully reviewed and selected from 30 submissions. The conference focuses on integrative machine learning approach, considering the importance of data science and visualization for the algorithmic pipeline with a strong emphasis on privacy, data protection, safety and security.

This book constitutes the refereed proceedings of the 29th International Conference on Collaboration Technologies and Social Computing, CollabTech 2023, held in Osaka, Japan, during August 29¿September 1, 2023, in hybrid mode.The 8 full papers presented in this book together with 12 short papers were carefully reviewed and selected from 31 submissions. The papers focus on innovative technical, human and organizational approaches to expand collaboration support including computer science, management science, design science, cognitive and social science.

This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are introduced. The new title of this handbook and its content reflect these changes thoroughly. Some existing chapters have been brought up to date. In addition to major revision of the existing chapters, the new edition includes totally new topics, such as: deep learning, explainable AI, human factors and social issues and advanced methods for big-data. The significant enhancement to the content reflects the growth in importance of data science. The third edition is also a timely opportunity to incorporate many other changes based on peers and students¿ feedback.This comprehensive handbook also presents a coherent and unified repository of data science major concepts, theories, methods, trends, challenges and applications. It covers all the crucial important machine learning methods used in data science.Today's accessibility and abundance of data make data science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for data science, methods originated from artificial intelligence, particularly machine learning, are also playing a significant role.This handbook aims to serve as the main reference for researchers in the fields of information technology, e-Commerce, information retrieval, data science, machine learning, data mining, databases and statistics as well as advanced level students studying computer science or electrical engineering. Practitioners working within these related fields and data scientists will also want to purchase this handbook as a reference.

Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool--a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse.

This book constitutes the refereed post proceedings of the 16th Research Conference onMetadata and Semantic Research, MTSR 2022, held in London, UK, during November 7¿11, 2022.The 21 full papers and 4 short papers included in this book were carefully reviewed andselected from 79 submissions. They were organized in topical sections as follows: metadata, linked data, semantics and ontologies - general session, and track on Knowledge IT Artifacts (KITA), Track on digital humanities and digital curation, and track on cultural collections and applications, track on digital libraries, information retrieval, big, linked, social & open data, and metadata, linked data, semantics and ontologies - general session, track on agriculture, food & environment, and metadata, linked Data, semantics and ontologies - general, track on open repositories, research information systems & data infrastructures, and metadata, linked data, semantics andontologies - general, metadata, linked data, semantics and ontologies - general session, and track on european and national projects.

This book systemically presents key concepts of multi-modal hashing technology, recent advances on large-scale efficient multimedia search and recommendation, and recent achievements in multimedia indexing technology. With the explosive growth of multimedia contents, multimedia retrieval is currently facing unprecedented challenges in both storage cost and retrieval speed. The multi-modal hashing technique can project high-dimensional data into compact binary hash codes. With it, the most time-consuming semantic similarity computation during the multimedia retrieval process can be significantly accelerated with fast Hamming distance computation, and meanwhile the storage cost can be reduced greatly by the binary embedding. The authors introduce the categorization of existing multi-modal hashing methods according to various metrics and datasets. The authors also collect recent multi-modal hashing techniques and describe the motivation, objective formulations, and optimization steps for context-aware hashing methods based on the tag-semantics transfer.

This book constitutes the refereed post-conference proceedings of the Third International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022, held in November 2022 in Braga, Portugal. The 8 full papers of AISCOVID-19 2022 were carefully selected from 21 submissions and present a comprehensive and up-to-date look at the intersection of COVID-19, big data, machine learning, deep learning, and healthcare. The theme of AISCOVID-19 2022 was Healthcare effective and efficient Solutions for COVID-19 that can be achieved using Artificial Intelligence and Computer-Assisted paradigms.

Design and implement a data lakehouse using technology-driven simplifications and generalizations. ¿The approach you will learn enables consolidating even incoherent data from multiple source systems across complex enterprise environments. The precise business question does not need to be known in advance and can even change over time. The approach lends itself well to federated, cooperating data mesh nodes. The individual components, called mini-marts, are like the "data part" of a data quantum and are interoperable. We describe data model blueprints to generalize dimensions with synonyms and facts at different granularities. Includes code examples using complex hierarchies as they exist in heterogenous real-world go-to-market organizations.

This book constitutes the papers presented at the Evaluation Track of the 8th China Conference on Health Information Processing, CHIP 2022, held in Hangzhou, China during October 21¿23, 2022.The 20 full papers included in this book were carefully reviewed and selected from 20 submissions. They were organized in topical sections as follows: text mining for gene-disease association semantic; medical causal entity and relation extraction; medical decision tree extraction from unstructured text; OCR of electronic medical document; clinical diagnostic coding.

5G and related digital revolutions will require tens of thousands of edge data centers. This book tells you how they work and how to get them built.We are in the middle of the edge computing revolution. Responding to demand for lower latency, telcos and others are moving servers and storage closer to end users-away from the "core" to "the edge." This requires the deployment of many thousands of tiny edge data centers.The edge is a big, growing business. Driven by 5G, connected vehicles, and industrial automation, the "edge economy" is projected to reach $4.1 trillion by 2030, with investment in edge data centers set to exceed $140 billion by 2028.What exactly is an edge data center? This book explains what they are and how they work. It's early in the edge computing life cycle, so there's time to get prepared for what's coming.If you work in an industry that's transforming through mobility, or any field that will leverage the edge for competitive advantage, this book will help you understand how the edge data center advances your strategic agenda.

This open access book presents a collection of papers focusing on current 3D research challenges in the domain of digital cultural heritage. 3D technologies find considerable use within the field of cultural heritage at the beginning of the 21st century, for example in the areas of data acquisition, modeling, archiving in local repositories, harvesting in digital libraries and their long-term preservation.This volume put emphasis on a number of challenges facing 3D research in the 2D/3D digitization of tangible objects and their transformation to digital/virtual/memory twins; the interplay of geometry, semantics and the recovery and management of knowledge in digital cultural heritage; the handling of 3D data via the Cloud on the Internet and mobile devices; the presentation of cultural heritage content in 3D to the general public; and the 3D reproduction of cultural heritage objects from virtual to real.