"This book covers a broad range of intelligence integration approaches in distributed knowledge systems, from Web-based systems through multi-agent and grid systems, ontology management to fuzzy approaches"--Provided by publisher.

As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.

Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. - Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types - Explains, in non-technical terms, the architecture and components required to perform data integration - Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"

Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.

This text weaves together the history, theory and research of body-mind psychotherapy. The author lays a foundation for an understanding of the connection between the body and the mind and how the therapist/student can work to integrate the two into a healthy, functioning whole. The book is centred around two main themes: integration of the body and mind to access an organic source of intelligence and recognition that our bodies are motivated at every level (even down to cellular and molecular level). It also recognizes that emotion is an inherent part of our bodily process.

This book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). It accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning. To the best of our knowledge, the integration of SI and ANN is the first attempt to integrate various aspects of both the independent research area into a single volume.

Synopsis of "Artificial Intelligence Integration for Business" by Dr. Constance Quigley and Erich V. Barlow "Artificial Intelligence Integration for Business" explores the crucial intersection of AI technology and organizational leadership. Dr. Constance Quigley and Erich V. Barlow address the urgent need for businesses to tackle data privacy, security, and ethical issues as they integrate AI. The book emphasizes the importance of robust security measures, employee education, strategic planning, clear policies, and expert input from ethics, law, and technology professionals. In the preface, the authors highlight the significance of integrity in technological applications and advocate for responsible AI adoption aligned with ethical standards. Professor Barlow’s background in IT security and AI implementation research is showcased alongside Dr. Quigley’s expertise in leveraging AI for brand transformation. They offer practical solutions for navigating AI integration challenges, emphasizing transparency and accountability. The introduction outlines the benefits of AI adoption, including enhanced efficiency and productivity through automation tools like predictive analytics and chatbots. These technologies streamline processes, reduce costs, and allow employees to focus on high-value activities. AI also enables personalized customer experiences through targeted marketing, increases customer loyalty, and aids in risk mitigation, fraud detection, and real-time compliance monitoring. In their concluding remarks, Quigley and Barlow stress the importance of understanding AI's impactful outcomes, especially regarding human rights and societal well-being. They advocate for innovative, collaborative strategies across industries, promoting long-term success and a culture of innovation aligned with core values and sustainable growth. "Artificial Intelligence Integration for Business" is a vital resource for businesses seeking to responsibly harness AI's potential, addressing ethical concerns, ensuring transparency, and advancing technological advancements for the greater good.

As sustainable energy becomes the future, integrating solar power into existing systems presents critical challenges. Intelligent solutions are required to optimize energy production while maintaining transparency, reliability, and trust in decision-making processes. The growing complexity of these systems calls for advanced technologies that can ensure efficiency while addressing the unique demands of renewable energy sources. Explainable Artificial Intelligence and Solar Energy Integration explores how Explainable AI (XAI) enhances transparency in AI-driven solutions for solar energy integration. By showcasing XAI's role in improving energy efficiency and sustainability, the book bridges the gap between AI potential and real-world solar energy applications. It serves as a comprehensive resource for researchers, engineers, policymakers, and students, offering both technical insights and practical case studies.