PERVASIVE computing 53Trust is situation-specific; trust in one environment doesn't directly transfer to another environment. So a notion of context is necessary.Authorized licensed use limited to: TRINITY COLLEGE DUBLIN. Downloaded on January 21, 2009 at 05:54 from IEEE Xplore. Restrictions apply.them in a particular way-for example, to update old address book entries with accurate information. However, the principal could deviate from this expected behavior, and the combined likelihood and severity of this is the risk of granting them a privilege. Risk analysisIn SECURE, the risks of a trust-mediated action are decomposed by possible outcomes. Each outcome's risk depends on the other principal's trustworthiness (the likelihood) and the outcome's intrinsic cost. For example, an address update might itself be out-of-date or maliciously misleading. These two outcomes' costs would reflect the user's wasted time, and the likelihoods would depend on trust in the other party.An outcome's costs could span a range of values. For example, a user might have received a correct phone book entry. This third outcome's cost could show a net benefit to the user, as the user might successfully use it later. However, if the number became out-ofdate by the time it was used, that would be a net loss. To reflect this uncertainty, you might represent the distribution of costs as a cost-PDF (probability density function). Figure 1 illustrates a user contemplating a parameterized interaction with principal p. For each possible outcome, the user has a parameterized cost-PDF (a family of cost-PDFs) that represents the range of possible costs and benefits the user might incur should each outcome occur.While the risk evaluator assesses the possible cost-PDFs, the trust calculator provides information t that determines the risk's likelihood based on the principal's identity p and other parameters of the action. The risk evaluator then uses this trust information to select the appropriate cost-PDF.Finally, the request analyzer combines all the outcomes' cost-PDFs to decide if the action should be taken or to arrange further interaction. Because any uncertainty is preserved right up to the decision point, this allows more complex decision making than simple thresholding, allowing responses such as "not sure" if there isn't enough information.In our continuing example, if Liz's PDA received a phone number from Vinny's PDA, she might not think it was maliciously misleading based on her trust in Vinny's honesty. She might think it could be out-of-date, however, if Vinny had given her stale information before, attributing a higher risk to this outcome. Finally, she'd consider the potential benefit of having a correct number, again moderated by Vinny's trustworthiness. Liz's PDA would do all these calculations on her behalf using its model of her trust beliefs, as Figure 2 illustrates. If the benefits outweighed the other outcomes' costs, the PDA would then accept the information.On the other hand, if John-a colleague from a competing research gr...

BackgroundContinual improvements in diagnostic processes are needed to minimise the proportion of patients with cancer who experience diagnostic delays. Clinical audit is a means of achieving this.AimTo characterise key aspects of the diagnostic process for cancer and to generate baseline measures for future re-audit.Design and settingClinical audit of cancer diagnosis in general practices in England.MethodInformation on patient and tumour characteristics held in the English National Cancer Registry was supplemented by information from GPs in participating practices. Data items included diagnostic timepoints, patient characteristics, and clinical management.ResultsData were collected on 17 042 patients with a new diagnosis of cancer during 2014 from 439 practices. Participating practices were similar to non-participating ones, particularly regarding population age, urban/rural location, and practice-based patient experience measures. The median diagnostic interval for all patients was 40 days (interquartile range [IQR] 15–86 days). Most patients were referred promptly (median primary care interval 5 days [IQR 0–27 days]). Where GPs deemed diagnostic delays to have occurred (22% of cases), patient, clinician, or system factors were responsible in 26%, 28%, and 34% of instances, respectively. Safety netting was recorded for 44% of patients. At least one primary care-led investigation was carried out for 45% of patients. Most patients (76%) had at least one existing comorbid condition; 21% had three or more.ConclusionThe findings identify avenues for quality improvement activity and provide a baseline for future audit of the impact of 2015 National Institute for Health and Care Excellence guidance on management and referral of suspected cancer.

Abstract. For large-scale distributed applications such as internet-wide or ubiquitous systems, event-based communication is an effective messaging mechanism between components. In order to handle the large volume of events in such systems, composite event detection enables application components to express interest in the occurrence of complex patterns of events. In this paper, we introduce a general composite event detection framework that can be added on top of existing middleware architectures -as demonstrated in our implementation over JMS. We argue that the framework is flexible, expressive, and easy to implement. Based on finite state automata extended with a rich time model and support for parameterisation, it provides a decomposable core language for composite event specification, so that composite event detection can be distributed throughout the system. We discuss the issues associated with automatic distribution of composite event expressions. Finally, tests of our composite event system over JMS show reduced bandwidth consumption and a low notification delay for composite events.

Event-based communication provides a flexible and robust approach to monitoring and managing large-scale distributed systems. Composite event detection extends the scope and flexibility of these systems, by allowing application components to express interest in complex patterns of events. This makes it possible to handle the large numbers of events generated in Internet-wide systems, and in network monitoring and pervasive computing applications. In this article we introduce a novel generic composite event detection framework that can be added on top of existing middleware architectures, as demonstrated in our implementation over JMS. We argue that the framework is flexible, expressive, and easy to implement. Based on finite state automata extended with a rich time model and parameterization support, it provides a decomposable core language for specifying composite events. This allows detection to be distributed automatically throughout the system, guided by distribution policies that control the quality of service. Finally, tests show that using our composite event system over JMS can reduce bandwidth consumption while maintaining low notification delay for composite events.