Policy-Based Autonomic Data Governance

Advances in artificial intelligence, sensor computing, robotics, and mobile systems are making autonomous systems a reality. At the same time, the influence of edge computing is leading to more distributed architectures incorporating more autonomous elements. The flow of information is critical in such environments, but the real time, distributed nature of the system components complicates the data protection mechanisms. Policy-based management has proven useful in simplifying the complexity of management in domains like networking, security, and storage, it is expected that many of those benefits would carry over to the task of managing big data and autonomous systems. This book aims at providing an overview of recent work and identifying challenges related to the design of policy-based approaches for managing big data and autonomous systems. An important new direction explored in the book is to make the major elements of the system self-describing and self-managing. This would lead to architectures where policy mechanisms are tightly coupled with the system elements. In such integrated architectures, we need new models for information assurance, traceability of information, and better provenance on information flows. In addition when dealing with devices with actuation capabilities and, thus, being able to make changes to physical spaces, safety is critical. With an emphasis on policy-based mechanisms for governance of data security and privacy, and for safety assurance, the papers in this volume follow three broad themes: foundational principles and use-cases for the autonomous generation of policies, safe autonomy, policies and autonomy in federated environments.

anomaly detection, artificial intelligence, complex systems, computer crime, computer networks, data mining, data security, databases, intrusion detection, large scale systems, semantics, sensors, telecommunication networks, telecommunication traffic