Propagation of Policies in Rich Data Flows

Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules

Towards a privacy aware information system for emergency response

Intelligent Systems in Smart Cities capture and exchange a large variety of information, for example, environmental data, location, biometric and personal data, health records, among others, in order to improve the quality of services. On one hand, such systems could represent an important, lifesaving resource for public services aimed at addressing emergency situations (e.g. firefighters, police), by providing access to a large amount of diverse information. On the other hand, they are also a threat with respect to data protection and privacy when disclosing all sort of personal and sensitive information. Since not all the information available can be used or helpful for handling the emergency we have the challenge of ensuring that the least possible amount of sensitive information is exchanged, therefore reducing the risk of unwanted disclosure and misuse. Thus, being aware and include a privacy-by design approach when managing personal and sensitive data is essential in the context of emergency systems. This work aims to analyse the privacy issues that Intelligent Systems face when sharing information with public services to attend emergency situations. By characterising these issues, we aim of informing the knowledge requirements for designing an Intelligent System that only allows valuable and helpful information to be exchanged, minimising personal and sensitive data disclosure

Governance of Autonomous Agents on the Web: Challenges and Opportunities

International audienceThe study of autonomous agents has a long tradition in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this paper motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities

Health Condition Evolution for Effective Use of Electronic Records: Knowledge Representation, Acquisition, and Reasoning

Smart City initiatives aim to enhance the effective management of resources while providing quality services to citizens. Central to these initiatives is the use of large-scale datasets that enable intelligent analytics and reasoning components in support of resource optimisation and service provision. Recently, there has been a growing interest in aspects of smart living, particularly due to the increasing adoption and use of Electronic Health Records (EHR). A Smart City can introduce intelligent systems to support the usage of EHR to improve emergency response services. For instance, data derived from EHR is used in primary emergency care, as a component of emergency decision support systems and for monitoring public health. However, the delivery of healthcare information to emergency bodies must be balanced against the concerns related to citizens’ privacy. Besides, emergency services face challenges in interpreting this data; the heterogeneity of sources and the large amount of available information represents a significant barrier. This thesis investigates the use of EHR for deriving useful information about people requiring assistance during an emergency, focusing on making rich data accessible to emergency services while minimising the amount of exchanged information. To perform this task, an intelligent system needs to estimate the probability that a potentially relevant condition mentioned in a health record is still valid at the time of the emergency. During our research work, we followed a knowledge engineering approach and developed the required knowledge components to support the intelligent delivery of relevant health information about people involved in an emergency situation. These components, which include a knowledge component for representation and reasoning, and a novel knowledge base modelling the evolution of a large number of health conditions, form the basis of CONRAD, a system which is able to support effectively decision-making in an emergency scenario

Propagation of Policies in Rich Data Flows

none1siGoverning the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data ow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules.noneDaga E, D'Aquin M, Gangemi A, Motta EDaga E, D'Aquin M, Gangemi A, Motta