Intelligence Management

Strategic Intelligence Management introduces both academic researchers and law enforcement professionals to contemporary issues of national security and information management and analysis. This contributed volume draws on state-of-the-art expertise from academics and law enforcement practitioners across the globe. The chapter authors provide background, analysis, and insight on specific topics and case studies. Strategic Intelligent Management explores the technological and social aspects of managing information for contemporary national security imperatives. Academic researchers and graduate students in computer science, information studies, social science, law, terrorism studies, and politics, as well as professionals in the police, law enforcement, security agencies, and government policy organizations will welcome this authoritative and wide-ranging discussion of emerging threats. Hot topics like cyber terrorism, Big Data, and Somali pirates, addressed in terms the layperson can understand, with solid research grounding Fills a gap in existing literature on intelligence, technology, and national security

The current rapid development in both computing power and the ability to present and mine complex data sets in useful ways provides the backdrop to Intelligence Management: Knowledge Driven Frameworks for Combating Terrorism and Organized Crime. The chapters address the linkage between: law enforcement; developments in information and communication technologies and key ideas about the management of data, information, knowledge and intelligence. The work is conducted by a number of international academic and industrial research groups, law enforcement agencies, and end users. Section 1 presents four chapters that address the details, outcomes, user needs and background theoretical ideas behind a large-scale research aand development project in this domain (The Odyssey Project). This project explored the challenges of establishing a Pan-European ballistics and crime information intelligence network. It represents an example of the type of system that is likely to become commonly used by Law Enforcement Agencies in the near future. Many of the challenges are not technical but organisational, legal, economic, social and political. Sections 2 and 3 therefore present wider commentaries. Section 2 explores other research and development projects that attempt to exploit the power of contemporary ICT systems to support Law Enforcement Agencies in many aspects of their work including investigations, data analysis and presentation, identification, training and crime prevention. Section 3 takes a look at the social and organisational issues around aspects of crime prevention, crime detection and policing – with a view to the role of information and communication technologies in these contexts.

Learn how to develop and employ an ontology, the secret weapon for successfully using artificial intelligence to create a powerful competitive advantage in your business. The AI-Powered Enterprise examines two fundamental questions: First, how will the future be different as a result of artificial intelligence? And second, what must companies do to stake their claim on that future? When the Web came along in the mid-90s, it transformed the behavior of customers and remade whole industries. Now, as part of its promise to bring revolutionary change in untold ways to human activity, artificial intelligence—AI—is about to create another complete transformation in how companies create and deliver value to customers. But despite the billions spent so far on bots and other tools, AI continues to stumble. Why can't it magically use all the data organizations generate to make them run faster and better? Because something is missing. AI works only when it understands the soul of the business. An ontology is a holistic digital model of every piece of information that matters to the business, from processes to products to people, and it's what makes the difference between the promise of AI and delivering on that promise. Business leaders who want to catch the AI wave—rather than be crushed by it—need to read The AI-Powered Enterprise. The book is the first to combine a sophisticated explanation of how AI works with a practical approach to applying AI to the problems of business, from customer experience to business operations to product development.

This book combines academic research with practical guidelines in methods and techniques to supplement existing knowledge relating to organizational management in the era of digital acceleration. It offers a simple layout with concise but rich content presented in an engaging, accessible style and the authors’ holistic approach is unique in the field. From a universalist perspective, the book examines and analyzes the development of, among others, Industry 4.0, artificial intelligence (AI), AI 2.0, AI systems and platforms, algorithmics, new paradigms of organization management, business ecosystems, data processing models in AI-based organizations and AI strategies in the global perspective. An additional strength of the book is its relevance and contemporary nature, featuring information, data, forecasts or scenarios reaching up to 2030. How does one build, step by step, an organization that will be based on artificial intelligence technology and gain measurable benefits from it, for instance, as a result of its involvement in the creation of the so-called mesh ecosystem? The answer to this and many other pertinent questions are provided in this book. This timely and important book will appeal to scholars and students across the fields of organizational management and innovation and technology management, as well as managers, educators, scientists, entrepreneurs, innovators and more.

Author Paul Boudreau shares the keys to project management success using a modern approach: artificial intelligence. Within the pages of Applying Artificial Intelligence to Project Management, Boudreau describes five AI tools in concept and how they apply directly to project success, as well as the strategy and method to use to purchase and implement AI tools for project management. Understand the difference between automating a task and changing it by using AI. Discover how AI uses data and the importance of data maintenance. Learn why projects fail and how using artificial intelligence for project management improves project success rates. Read project management success stories in one of the best business books on machine learning, and prepare to leave behind that 50 percent project success rate for one that's 95 percent or higher.

This book offers a comprehensive reference guide to intelligence systems in environmental management. It provides readers with all the necessary tools for solving complex environmental problems, where classical techniques cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including ant colony, genetic algorithms, evolutionary algorithms, fuzzy multi-criteria decision making tools, particle swarm optimization, agent-based modelling, artificial neural networks, simulated annealing, Tabu search, fuzzy multi-objective optimization, fuzzy rules, support vector machines, fuzzy cognitive maps, cumulative belief degrees, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on complex environmental problems. Moreover, by extending all the main aspects of classical environmental solution techniques to its intelligent counterpart, the book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

Nearly every large corporation and governmental agency is taking a fresh look at their current enterprise-scale business intelligence (BI) and data warehousing implementations at the dawn of the "Big Data Era"...and most see a critical need to revitalize their current capabilities. Whether they find the frustrating and business-impeding continuation of a long-standing "silos of data" problem, or an over-reliance on static production reports at the expense of predictive analytics and other true business intelligence capabilities, or a lack of progress in achieving the long-sought-after enterprise-wide "single version of the truth" – or all of the above – IT Directors, strategists, and architects find that they need to go back to the drawing board and produce a brand new BI/data warehousing roadmap to help move their enterprises from their current state to one where the promises of emerging technologies and a generation’s worth of best practices can finally deliver high-impact, architecturally evolvable enterprise-scale business intelligence and data warehousing. Author Alan Simon, whose BI and data warehousing experience dates back to the late 1970s and who has personally delivered or led more than thirty enterprise-wide BI/data warehousing roadmap engagements since the mid-1990s, details a comprehensive step-by-step approach to building a best practices-driven, multi-year roadmap in the quest for architecturally evolvable BI and data warehousing at the enterprise scale. Simon addresses the triad of technology, work processes, and organizational/human factors considerations in a manner that blends the visionary and the pragmatic. Takes a fresh look at true enterprise-scale BI/DW in the "Dawn of the Big Data Era" Details a checklist-based approach to surveying one’s current state and identifying which components are enterprise-ready and which ones are impeding the key objectives of enterprise-scale BI/DW Provides an approach for how to analyze and test-bed emerging technologies and architectures and then figure out how to include the relevant ones in the roadmaps that will be developed Presents a tried-and-true methodology for building a phased, incremental, and iterative enterprise BI/DW roadmap that is closely aligned with an organization’s business imperatives, organizational culture, and other considerations

Artificial intelligence (AI) has driven businesses to adopt new business practices rapidly, enhance product development and services, has helped to power AI-based market intelligence and customer insights, and improve customer relationship management. This timely book addresses the use of AI in marketing. This book also explores the dark side of AI in marketing management and discusses ethics and transparency of automated decision-making in AI applications, data privacy, cyber security issues, and biases in various facets of marketing. Emerging applications of AI such as DeepFakes which use deep learning technology could increase risks of manipulation and deception. Hence, apart from leveraging AI capabilities and advantages, the book cautions the need for prevention strategies to deal with potential issues that could arise from the adoption of AI in marketing management. This book will provide practical insights into the role of AI in marketing management. It will be a useful reference for those researching marketing and marketing professionals.