With the growing importance of data in all aspects of the functioning of an enterprise, having good quality of data is crucial in support of business processes. However, there do not exist good metrics to measure the quality of data that is available within an enterprise. While there are several data quality standards, their complexity and their required customization makes them difficult to use in real-world industrial scenarios. In this paper, we discuss the challenges encountered in measuring data quality within asset management systems. We propose a policy-based approach for measuring data quality, and show how such an approach can be customized and interpreted easily by practitioners in the field.

Acoustic signals contain rich information of the environment. They can be used for detecting anomalous events such as in automated machine monitoring. In this demonstration, we present our acoustic anomaly detection system that captures acoustic signals and classifies them using machine learning techniques. Our system includes a server for sound management and model training, a mobile client for sound capturing and real-time classification, and a workbench that acts as a user interface. We will show the full operational pipeline of our system in this demonstration.

Policy-based mechanisms are used to implement desired autonomic behavior of a managed system in a distributed environment. For modern dynamically changing systems, policy-based mechanisms tend to be too rigid, and quickly lose their efficacy when conditions of the autonomous system change during its operation. In this paper, we propose a generative policy framework that can generate policies for an autonomous system when conditions change. For changed conditions, the policy generation manager dynamically generates new set of policies optimized for the new situation. As a use case, we demonstrate how our generative policy framework generates policies for selecting optimal data for an AI model training. The policies are dynamically generated based on the availability and trustworthiness of data in a coalition environment.

Policies which address security and privacy are pervasive parts of both technical and social systems, and technology to enable both organizations and individuals to create and manage such policies is seen as a critical need in IT. This paper describes policy authoring as a key component to usable privacy and security systems, and advances the notions of policy templates in a policy management environment in which different roles with different skill sets are seen as important. We discuss existing guidelines and provide support for the addition of new guidelines for usable policy authoring for security and privacy systems. We describe the relationship between general policy templates and specific policies, and the skills necessary to author each of these in a way that produces high-quality policies. We also report on an experiment in which technical users with limited policy experience authored policy templates using a prototype template authoring user interface we developed.