Improving Resource Discovery in the Arigatoni Overlay Network

International audienceArigatoni is a structured multi-layer overlay network providing various services with variable guarantees, and promoting an intermittent participation to the virtual organization where peers can appear, disappear and organize themselves dynamically. Arigatoni mainly concerns with how resources are declared and discovered in the overlay, allowing global computers to make a secure, PKI-based, use of global aggregated computational power, storage, information resources, etc. Arigatoni provides fully decentralized, asynchronous and scalable resource discovery, and provides mechanisms for dealing with dynamic virtual organizations. This paper introduces a non trivial improvement of the original resource discovery protocol by allowing to register and to ask for multiple instances. Simulations show that it is efficient and scalable

Evaluating Tree Pattern Similarity for Content-based Routing Systems

With the advent of XML as the de facto language for data interchange, scalable distribution of data to large populations of consumers remains an important challenge. Content-based publish/subscribe systems offer a convenient abstraction for data producer and consumers, as most of the complexity related to addressing and routing is encapsulated within the network infrastructure. Data consumers typically specify their subscriptions using some XML pattern specification language (e.g., XPath), while producers publish content without prior knowledge of the recipients, if any. A novel approach to content-based routing consists in organizing consumers with similar interests in peer-to-peer semantic communities inside which XML documents are propagated. In order to build semantic communities and connect peers that share common interests with each other, one needs to evaluate the similarity between their subscriptions. In this paper, we specifically address this problem and we propose novel algorithms to compute the similarity of seemingly unrelated tree patterns by taking advantage of information derived from the XML document types, such as valid combinations of elements, or conjunctions and disjunctions on their occurrence. These results are of interest in their own right, and can prove useful in other domains, such as approximate XML queries involving tree patterns. Results from a prototype implementation validate the effectiveness of our approach

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Tree-Pattern Similarity Estimation for Scalable Content-based Routing

With the advent of XML as the de facto language for data publishing and exchange, scalable distribution of XML data to large, dynamic populations of consumers remains an important challenge. Content-based publish/subscribe systems offer a convenient design paradigm, as most of the complexity related to addressing and routing is encapsulated within the network infrastructure. To indicate the type of content that they are interested in, data consumers typically specify their subscriptions using a tree-pattern specification language (an important subset of XPath), while producers publish XML content without prior knowledge of any potential recipients. Discovering semantic communities of consumers with similar interests is an important requirement for scalable content-based systems: such “semantic clusters ” of consumers play a critical role in the design of effective content-routing protocols and architectures. The fundamental problem underlying the discovery of such semantic communities lay in effectively evaluating the similarity of different tree-pattern subscriptions based on the observed document stream. In this paper, we propose a general framework and algorithmic tools for estimating different tree-pattern similarity metrics over continuous streams of XML documents. In a nutshell, our approach relies on continuously maintaining a novel, concise synopsis structure over the observed document stream that allows us to accurately estimate the fraction of documents satisfying various boolean combinations of different tree-pattern subscriptions. To effectively capture different branching and correlation patterns within a limited amount of space, our techniques use ideas from hash-based sampling in a novel manner that exploits the hierarchical structure of our document synopsis. Experimental results with various XML data streams verify the effectiveness of our approach. 1

XNET: A Reliable Content-Based Publish/Subscribe System

Content-based publish/subscribe systems are usually implemented as a network of brokers that collaboratively route messages from information providers to consumers. A major challenge of such middleware infrastructures is their reliability and their ability to cope with failures in the system. In this paper, we present the architecture of the XNET XML content network and we detail the mechanisms that we implemented to gracefully handle failures and maintain the system state consistent with the consumer population at all times. In particular, we propose several approaches to fault tolerance so that our system can recover from various types of router and link failures. We analyze the efficiency of our techniques in a large scale experimental deployment on the PlanetLab testbed. We show that XNET does not only offer good performance and scalability with large consumer populations under normal operation, but can also quickly recover from system failures

Dissémination d'information à large échelle dans les systèmes de type publication-abonnement

NICE-BU Sciences (060882101) / SudocNANCY-INRIA Lorraine LORIA (545472304) / SudocSudocFranceF