Reliable Composite Web Services Execution: Towards a Dynamic Recovery Decision

AbstractDuring the execution of a Composite Web Service (CWS), different faults may occur that cause WSs failures. There exist strategies that can be applied to repair these failures, such as: WS retry, WS substitution, compensation, roll-back, or replication. Each strategy has advantages and disadvantages on different execution scenarios and can produce different impact on the CWS QoS. Hence, it is important to define a dynamic fault tolerant strategy which takes into account environment and execution information to accordingly decide the appropriate recovery strategy. We present a preliminary study in order to analyze the impact on the CWS total execution time of different recovery strategies on different scenarios. The experimental results show that under different conditions, recovery strategies behave differently. This analysis represents a first step towards the definition of a model to dynamically decide which recovery strategy is the best choice by taking into account the context-information when the failure occurs

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Simaweb: una aplicacion web para el procesamiento de imágenes en un ambiente distribuido

En este trabajo se presenta el desarrollo de una Aplicación Web (SIMAWEB) que interactúa en tiempo real con un Sistema de Procesamiento de Imágenes en un Ambiente Distribuido (SIMA) para la aplicación de operaciones sobre imágenes. SIMAWEB permite el procesamiento de múltiples imágenes distribuidas sobre una red heterogénea a través de la Web, aprovechando al máximo los recursos disponibles sobre una red local de computadores. Para la evaluación de SIMAWEB se llevó a cabo un estudio de usabilidad mediante una técnica rápida y económica de evaluación basada en el usuario, que permitió identificar y solucionar algunos problemas, y determinar que la navegación es bastante sencilla para usuarios conocedores de sistemas de procesamiento de imágenes.Eje: Arquitectura, Redes y Sistemas Operativos (ARSO)Red de Universidades con Carreras en Informática (RedUNCI

Big Data Analytic Approaches Classification

Analytical data management applications, affected by the explosion of the amount of generated data in the context of Big Data, are shifting away their analytical databases towards a vast landscape of architectural solutions combining storage techniques, programming models, languages, and tools. To support users in the hard task of deciding which Big Data solution is the most appropriate according to their specific requirements, we propose a generic architecture to classify analytical approaches. We also establish a classification of the existing query languages, based on the facilities provided to access the Big Data architectures. Moreover, to evaluate different solutions, we propose a set of criteria of comparison, such as OLAP support, scalability, and fault tolerance support. We classify different existing Big Data analytics solutions according to our proposed generic architecture and qualitatively evaluate them in terms of the criteria of comparison. We illustrate howour proposed generic architecture can be used to decide which Big Data analytic approach is suitable in the context of several use cases

Storage And Management Of Similar Images

Numerical images are becoming more and more important and an increasing emphasis on multimedia applications has resulted in large volumes of images. However, images need a large memory space to be stored, so their efficient storage and retrieval generate challenges to the database community. This paper proposes a new algorithm for an efficient storage of sets of images. It is based on a version approach used in databases. It shows how to store and operate on similar images; two images are defined as similar if the quad-trees encoding them have only few different nodes. A data structure called Generic Quad-Tree (GQT) is proposed. It optimizes the memory space required to store similar images and allows an efficient navigation among them. An Image Tree stores the ancestors and descendants of an image, like a version hierarchy. Using the Image Tree, the Generic Quad-Tree allows an image to share common parts with its ancestors and descendants. The GQT approach and some algorithms for reading, modifying or removing images from the Generic Quad-Tree are described. Examples using black and white images and gray scale images are presented

Synthèse sur les modèles de représentation des relations spatiales dans les images symboliques

National audienc

Embedding contextual spatial relationships in image retrieval by visual content

Cette thèse est consacrée à l'étude de méthodes de recherche par similarité des images dans une collection. Les images auxquelles nous nous intéressons sont quelconques, ce qui implique que les processus que nous utilisons doivent pouvoir s'exécuter sans être contraints par un quelconque modèle d'apparence visuelle. Nous nous sommes appuyés sur les relations spatiales entre les entités présentes dans les images qui peuvent être des objets symboliques ou des descripteurs visuels.La première partie de cette thèse est consacrée à une synthèse des techniques de relations spatiales. Dans la suite de cette étude, nous proposons notre approche -TSR, notre première contribution, qui permet de faire une recherche par similarité de contenu visuel en utilisant les relations triangulaires entre les entités dans les images. Dans nos expériences, les entités sont des caractéristiques visuelles locales basées sur les points saillants représentés dans le modèle Bag-Of-Features. Cette approche améliore non seulement la qualité des images retournées mais aussi le temps d'exécution par rapport à des approches de la littérature.La seconde partie est dédiée à l'étude du contexte de l'image. L'ensemble des relations entre les entités dans une image permet de produire une description globale que nous appelons le contexte. La prise en compte des relations spatiales contextuelles dans la recherche par similarité des images pourraient permettre d'améliorer la qualité de recherche en limitant les fausses alarmes. Dans le cadre de notre travail, nous avons défini le contexte d'image par la présence des catégories d'entité et leurs relations spatiales dans l'image. Nous avons étudié les différentes relations entre les catégories d'entité d'une base d'images symboliques de contenu hétérogène. Cette étude statistique, notre deuxième contribution, nous permet de créer une cartographie de leurs relations spatiales. Elle peut être intégrée dans un graphe de connaissance des relations contextuelles, notre troisième contribution. Ce graphe permet de décrire de façon générale des connaissances sur les catégories d'entité. Le raisonnement spatial sur ce graphe de connaissance peut nous aider à améliorer les tâches dans le traitement d'image comme la détection et la localisation d'une catégorie à l'aide de la présence d'une autre référence. Pour la suite, ce modèle peut être appliqué à représenter le contexte d'une image. La recherche par similarité basée sur le contexte peut être réalisée par la comparaison de graphes. La similarité contextuelle des deux images est la similarité de leurs graphes. Ce travail a été évalué sur la base d'images symboliques LabelMe. Les expériences ont montré sa pertinence pour la recherche d'images par le contexteThis thesis is focused on the study of methods for image retrieval by visual content in collection of heterogeneous contents. We are interested in the description of spatial relationships between the entities present in the images that can be symbolic objects or visual primitives such as interest points. The first part of this thesis is dedicated to a state of the art on the description of spatial relationship techniques. As a result of this study, we propose the approach -TSR, our first contribution, which allows similarity search based on visual content by using the triangular relationships between entities in images. In our experiments, the entities are local visual features based on salient points represented in a bag of features model. This approach improves not only the quality of the images retrieval but also the execution time in comparison with other approaches in the literature. The second part is dedicated to the study of the image context. The spatial relationships between entities in an image allow creating the global description of the image that we call the image context. Taking into account the contextual spatial relationships in the similarity search of images can allow improving the retrieval quality by limiting false alarms. We defined the context of image as the presence of entity categories and their spatial relationships in the image. We studied the relationships between different entity categories on LabelMe, a state of the art of symbolic images databases of heterogeneous content. This statistical study, our second contribution, allows creating a cartography of their spatial relationships. It can be integrated in a graph-based model of the contextual relationships, our third contribution. This graph describes the general knowledge of every entity categories. Spatial reasoning on this knowledge graph can help improving tasks of image processing such as detection and localization of an entity category by using the presence of another reference. Further, this model can be applied to represent the context of an image. The similarity search based on context can be achieved by comparing the graphs, then, contextual similarity between two images is evaluated by the similarity between their graphs. This work was evaluated on the symbolic image database of LabelMe. The experiments showed its relevance for image retrieval by contextPARIS-DAUPHINE-BU (751162101) / SudocSudocFranceF

Large Scale Disk-Based Metric Indexing Structure for Approximate Information Retrieval by Content

In order to achieve large scalability, indexing structures are usually distributed to incorporate more of expensive main memory during the query processing. In this paper, an indexing structure, that does not suffer from a performance degradation by its transition from main memory storage to hard drive, is proposed. The high efficiency of the index is achieved using a very effective pruning based on precomputed distances and so called locality phenomenon which substantially diminishes the number of retrieved candidates. The trade-offs for the large scalability are, firstly, the approximation and, secondly, longer query times, yet both are still bearable enough for recent multimedia content-based search systems, proved by an evaluation using visual and audio data and both metric and semi-metric distance functions. The tuning of the index’s parameters based on the analysis of the particular’s data intrinsic dimensionality is also discussed.