Path constraints in semistructured data

International audienceWe consider semistructured data as multirooted edge-labelled directed graphs, and path inclusion constraints on these graphs. A path inclusion constraint pnot precedes, equalsq is satisfied by a semistructured data if any node reached by the regular query p is also reached by the regular query q. In this paper, two problems are mainly studied: the implication problem and the problem of the existence of a finite exact model. - We give a new decision algorithm for the implication problem of a constraint pnot precedes, equalsq by a set of bounded path constraints pinot precedes, equalsui where p, q, and the pi's are regular path expressions and the ui's are words, improving in this particular case, the more general algorithms of S. Abiteboul and V. Vianu, and N. Alechina et al. In the case of a set of word equalities ui≡vi, we provide a more efficient decision algorithm for the implication of a word equality u≡v, improving the more general algorithm of P. Buneman et al. We prove that, in this case, implication for nondeterministic models is equivalent to implication for (complete) deterministic ones. - We introduce the notion of exact model: an exact model of a set of path constraints Click to view the MathML source satisfies the constraint pnot precedes, equalsq if and only if this constraint is implied by Click to view the MathML source. We prove that any set of constraints has an exact model and we give a decidable characterization of data which are exact models of bounded path inclusion constraints sets

An adaptive multi-agent system for task reallocation in a MapReduce job

International audienceWe study the problem of task reallocation for load-balancing of MapReduce jobs in applications that process large datasets. In this context, we propose a novel strategy based on cooperative agents used to optimise the task scheduling in a single MapReduce job. The novelty of our strategy lies in the ability of agents to identify opportunities within a current unbalanced allocation, which in turn trigger concurrent and one-to-many negotiations amongst agents to locally reallocate some of the tasks within a job. Our contribution is that tasks are reallocated according to the proximity of the resources and they are performed in accordance to the capabilities of the nodes in which agents are situated. To evaluate the adaptivity and responsiveness of our approach, we implement a prototype test-bed and conduct a vast panel of experiments in a heterogeneous environment and by exploring varying hardware configurations. This extensive experimentation reveals that our strategy significantly improves the overall runtime over the classical Hadoop data processing

Allocation équitable de tâches pour l'analyse de données massives

L'URL de l'ouvrage est la suivante:http://www.cepadues.com/livres/jfsma-2016-systemes-multi-agents-simulations-9782364935594.htmlInternational audienceMany companies are using MapReduce applications to process very large amounts of data. Static optimization of such applications is complex because they are based on user-defined operations, called map and reduce, which prevents some algebraic optimization. In order to optimize the task allocation, several systems collect data from previous runs and predict the performance doing job profiling. However they are not effective during the learning phase, or when a new type of job or data set appears. In this paper, we present an adaptive multiagent system for large data sets analysis with MapReduce. We do not preprocess data and we adopt a dynamic approach, where the reducer agents interact during the job. In order to decrease the workload of the most loaded reducer - and so the execution time - we propose a task re-allocation based on negotiation.De nombreuses entreprises utilisent l'application MapReduce pour le traitement de données massives. L'optimisation statique de telles applications est complexe car elles reposent sur des opérations définies par l'utilisateur, appelées map et reduce, ce qui empêche une optimisation algébrique. Afin d'optimiser l'allocation des tâches, plusieurs systèmes collectent des données à partir des exécutions précédentes et prédisent les performances en faisant une analyse de la tâche. Cependant, ces systèmes ne sont pas efficaces durant la phase d'apprentissage ou lorsqu'un nouveau type de tâches ou de données apparait. Dans ce papier, nous présentons un système multi-agents adaptatif pour l'analyse de données massives avec MapReduce. Nous ne pré-traitons pas les données et adoptons une approche dynamique où les agents reducers interagissent durant l'exécution. Nous proposons une ré-allocation des tâches basée sur la négociation pour parvenir à faire décroitre la charge de travail du plus chargé des agents reducers et ainsi réduire le temps d'exécution

XML Security Views Revisited

International audienceIn this paper, we revisit the view based security framework for XML without imposing any of the previously considered restrictions on the class of queries, the class of DTDs, and the type of annotations used to dene the view. First, we show that the full class of Regular XPath queries is closed under query rewriting. Next, we address the problem of constructing a DTD that describes the view schema, which in general needs not be regular. We propose three dierent methods of ap- proximating the view schema and we show that the produced DTDs are indistinguishable from the exact schema (with queries from a class speci c for each method). Finally, we investigate problems of static analysis of security access specications

Static analysis of XML security views and query rewriting

International audienceIn this paper, we revisit the view based security framework for XML without imposing any of the previously considered restrictions on the class of queries, the class of DTDs, and the type of annotations used to define the view. First, we study {\em query rewriting} with views when the classes used to define queries and views are Regular XPath and MSO. Next, we investigate problems of {\em static analysis} of security access specifications (SAS): we introduce the novel class of \emph{interval-bounded} SAS and we define three different manners to compare views (i.e. queries), with a security point of view. We provide a systematic study of the complexity for deciding these three comparisons, when the depth of the XML documents is bounded, when the document may have an arbitrary depth but the queries defining the views are restricted to guarantee the interval-bounded property, and in the general setting without restriction on queries and document

Modalités d'émergence et procédures de résolution des conflits d'usage autour de l'espace et des ressources naturelles. Analyse dans les espaces ruraux

rapport de recherche au programme Environnement, vie, sociétés du CNRS - appel d'offres "la gouvernance en question"conflits d'usage - aménagements - environnement - urbanisme

Pericentric heterochromatin reprogramming by new histone variants during mouse spermiogenesis

During male germ cell postmeiotic maturation, dramatic chromatin reorganization occurs, which is driven by completely unknown mechanisms. For the first time, we describe a specific reprogramming of mouse pericentric heterochromatin. Initiated when histones undergo global acetylation in early elongating spermatids, this process leads to the establishment of new DNA packaging structures organizing the pericentric regions in condensing spermatids. Five new histone variants were discovered, which are expressed in late spermiogenic cells. Two of them, which we named H2AL1 and H2AL2, specifically mark the pericentric regions in condensing spermatids and participate in the formation of new nucleoprotein structures. Moreover, our investigations also suggest that TH2B, an already identified testis-specific H2B variant of unknown function, could provide a platform for the structural transitions accompanying the incorporation of these new histone variants

A Location-Aware Strategy for Agents Negotiating Load-balancing

International audienceWe study a novel location-aware strategy for distributed systems where cooperating agents perform the load-balancing. The strategy allows agents to identify opportunities within a current unbalanced allocation , which in turn triggers concurrent and one-to-many negotiations amongst agents to locally reallocate some tasks. The tasks are reallocated according to the proximity of the resources and they are performed in accordance with the capabilities of the nodes in which agents are situated. This dynamic and ongoing negotiation process takes place concurrently with the task execution and so the task allocation process is adaptive to disruptions (task consumption, slowing down nodes). We evaluate the strategy in a multi-agent deployment of the MapReduce design pattern for processing large datasets. Empirical results demonstrate that our strategy significantly improves the overall runtime of the data processing

Stratégie situationnelle pour l'équilibrage de charge

National audienceWe study a novel location-aware strategy for distributed systems where cooperating agents perform the load-balancing. The strategy allows agents to identify opportunities within a current unbalanced allocation, which in turn triggers concurrent and one-to-many negotiations amongst agents to locally reallocate some tasks. The tasks are reallocated according to the proximity of the resources and they are performed in accordance with the capabilities of the nodes in which agents are situated. This dynamic and ongoing negotiation process takes place concurrently with the task execution and so the task allocation process is adaptive to disruptions (task consumption, slowing down nodes). We evaluate the strategy in a multi-agent deployment of the MapReduce design pattern for processing large datasets. Empirical results demonstrate that our strategy significantly improves the overall runtime of the data processing.Nous étudions une stratégie qui tient compte de la localité des ressources pour équilibrer les charges dans un système distribué. Cette stratégie permet aux agents coopératifs d'identifier une allocation non équilibrée, voire de déclencher des enchères concurrentes pour réallouer localement certaines des tâches. Les tâches sont réallouées en tenant compte de l'accessibilité des ressources pour les agents ; elles sont exécutées conformément aux capacités des noeuds de calcul sur lesquels se trouvent les agents. Ce processus de négociation dynamique et continu est concurrent à l'exécution des tâches, ce qui permet d'adapter l'allocation des tâches aux perturbations (exécution de tâche, chute de performance d'un nœud). Nous évaluons cette stratégie dans le cadre du déploiement multi-agents de MapReduce. Ce patron de conception permet le traitement distribué de données massives. Les résultats empiriques démontrent que notre stratégie améliore significativement le temps d'exécution du traitement d'un jeu de données