The enormous set of health and wellbeing data sources, as well as the diversity of the data, calls for an effective, time-aware integration paradigm that aids at the manipulation of the information by experts as a whole and not as individual pieces of knowledge. In this paper, we present the Health Service Bus, a service-based platform built on top of the Enterprise Service Bus architecture. Treating new information, either humangenerated (e.g., doctors, dieticians, etc.) or device-generated (i.e., smart wristbands or connected scales) as events allows for in-time action and treatment. Platform interoperability is ensured both on service level, since any service irrespective of its specification can be plugged into the Health Service Bus seamlessly, and on data level, since health standards, such as HL7 FHIR and LOINC, are leveraged.
Health monitoring devices let users monitor their health and habitual parameters. A health avatar can act as the electronic equivalent of a human to provide a dynamic life profile corresponding to the owner's physical status, living conditions, and habits.A plethora of newly available personal devices, mainly wearables, lets users measure their activities and health status and could easily be employed to provide health parameter monitoring and living condition evaluations, thus reducing users' visits to practitioners or health experts. At present, however, tools and applications for these devices are each linked to a particular solution. Realizing comprehensive health monitoring and assessment requires a single framework over which personal health status and living conditions can be tracked while maintaining users' privacy.We present the idea of a health avatar, the electronic representation of a human that reports its owner's physical status, living conditions, and habits as they're recorded through personal and wearable devices and sensors. This information is then processed and reasoned about via an ontological framework for personal health and wellbeing management. By adopting the successful model of online social network interaction, our platform provides a flexible communication medium that integrates and connects practitioners, patients, and virtual entities such as decisionsupport systems into a community for improving the quality of personal health. Our approach deploys state-of-the-art techniques for seamlessly discovering and collecting health and lifelog data (HLD) using a wide range of wearable devices, Wearable Computing
As the use of the Web expands and appears almost everywhere in business' practices, new algorithmic problems appear and need to be efficiently handled; one of them that has attracted the attention of both researchers and practitioners is click fraud. Click fraud can be defined as the practice of repetitively clicking on search ads without being actually interested in the content of the related links, with the intention of either increasing the Website's profits or exhausting an advertiser's budget. In this work, we propose an algorithm, which exposes suspect networks instead of single IPs, based on utilizing efficient data structures that have not been employed in previous works.
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