Minimum maximum-degree publish-subscribe overlay network design

Designing an overlay network for publish/subscribe communication in a system where nodes may subscribe to many different topics of interest is of fundamental importance. For scalability and efficiency, it is important to keep the degree of the nodes in the publish/subscribe system low. It is only natural then to formalize the following problem: Given a collection of nodes and their topic subscriptions, connect the nodes into a graph that has least possible maximum degree in such a way that for each topic t, the graph induced by the nodes interested in t is connected. We present the first polynomial-time logarithmic approximation algorithm for this problem and prove an almost tight lower bound on the approximation ratio. Our experimental results show that our algorithm drastically improves the maximum degree of publish/subscribe overlay systems. We also propose a variation of the problem by enforcing that each topic-connected overlay network be of constant diameter while keeping the average degree low. We present three heuristics for this problem that guarantee that each topic-connected overlay network will be of diameter 2 and that aim at keeping the overall average node degree low. Our experimental results validate our algorithms, showing that our algorithms are able to achieve very low diameter without increasing the average degree by much. © 2011 IEEE

Top-k/w publish/subscribe: finding k most relevant publications in sliding time window w

Existing content-based publish/subscribe systems are designed assuming that all matching publications are equally relevant to a subscription. As we cannot know in advance the distribution of publication content, the following two unwanted situations are highly possible: a subscriber either receives too many or only few publications. In this paper we present a new publish/subscribe model which is based on the sliding window computation model. Our model assumes that publications have different relevance to a subscription. In the model, a subscriber receives k most relevant publications published within a time window w, where k and w are parameters defined per each subscription. As a consequence, the arrival rate of incoming relevant publications per subscription is predefined, and does not depend on the publication rate. Since all relevant objects (i.e. publications in our case) cannot be kept in main memory, existing solutions immediately discard less relevant objects, and store only a small representative set for subsequent delivery. In this paper we develop a probabilistic criterion to decide upon the arrival of a new object whether it may become the top-k object at some future point in time and should thus be stored in a special publications queue. We show that by accepting typically very small probability of error, the queue length is reasonably small and does not significantly depend on publication rate. Furthermore, we experimentally evaluate our approach to demonstrate its applicability in practice

Semantic Annotation of Mutable Data

Electronic annotation of scientific data is very similar to annotation of documents. Both types of annotation amplify the original object, add related knowledge to it, and dispute or support assertions in it. In each case, annotation is a framework for discourse about the original object, and, in each case, an annotation needs to clearly identify its scope and its own terminology. However, electronic annotation of data differs from annotation of documents: the content of the annotations, including expectations and supporting evidence, is more often shared among members of networks. Any consequent actions taken by the holders of the annotated data could be shared as well. But even those current annotation systems that admit data as their subject often make it difficult or impossible to annotate at fine-enough granularity to use the results in this way for data quality control. We address these kinds of issues by offering simple extensions to an existing annotation ontology and describe how the results support an interest-based distribution of annotations. We are using the result to design and deploy a platform that supports annotation services overlaid on networks of distributed data, with particular application to data quality control. Our initial instance supports a set of natural science collection metadata services. An important application is the support for data quality control and provision of missing data. A previous proof of concept demonstrated such use based on data annotations modeled with XML-Schema

Techniques efficaces basées sur des vues matérialisées pour la gestion des données du Web (algorithmes et systèmes)

Le langage XML, proposé par le W3C, est aujourd hui utilisé comme un modèle de données pour le stockage et l interrogation de grands volumes de données dans les systèmes de bases de données. En dépit d importants travaux de recherche et le développement de systèmes efficace, le traitement de grands volumes de données XML pose encore des problèmes des performance dus à la complexité et hétérogénéité des données ainsi qu à la complexité des langages courants d interrogation XML. Les vues matérialisées sont employées depuis des décennies dans les bases de données afin de raccourcir les temps de traitement des requêtes. Elles peuvent être considérées les résultats de requêtes pré-calculées, que l on réutilise afin d éviter de recalculer (complètement ou partiellement) une nouvelle requête. Les vues matérialisées ont fait l objet de nombreuses recherches, en particulier dans le contexte des entrepôts des données relationnelles.Cette thèse étudie l applicabilité de techniques de vues matérialisées pour optimiser les performances des systèmes de gestion de données Web, et en particulier XML, dans des environnements distribués. Dans cette thèse, nos apportons trois contributions.D abord, nous considérons le problème de la sélection des meilleures vues à matérialiser dans un espace de stockage donné, afin d améliorer la performance d une charge de travail des requêtes. Nous sommes les premiers à considérer un sous-langage de XQuery enrichi avec la possibilité de sélectionner des noeuds multiples et à de multiples niveaux de granularités. La difficulté dans ce contexte vient de la puissance expressive et des caractéristiques du langage des requêtes et des vues, et de la taille de l espace de recherche de vues que l on pourrait matérialiser.Alors que le problème général a une complexité prohibitive, nous proposons et étudions un algorithme heuristique et démontrer ses performances supérieures par rapport à l état de l art.Deuxièmement, nous considérons la gestion de grands corpus XML dans des réseaux pair à pair, basées sur des tables de hachage distribuées. Nous considérons la plateforme ViP2P dans laquelle des vues XML distribuées sont matérialisées à partir des données publiées dans le réseau, puis exploitées pour répondre efficacement aux requêtes émises par un pair du réseau. Nous y avons apporté d importantes optimisations orientées sur le passage à l échelle, et nous avons caractérisé la performance du système par une série d expériences déployées dans un réseau à grande échelle. Ces expériences dépassent de plusieurs ordres de grandeur les systèmes similaires en termes de volumes de données et de débit de dissémination des données. Cette étude est à ce jour la plus complète concernant une plateforme de gestion de contenus XML déployée entièrement et testée à une échelle réelle.Enfin, nous présentons une nouvelle approche de dissémination de données dans un système d abonnements, en présence de contraintes sur les ressources CPU et réseau disponibles; cette approche est mise en oeuvre dans le cadre de notre plateforme Delta. Le passage à l échelle est obtenu en déchargeant le fournisseur de données de l effort de répondre à une partie des abonnements. Pour cela, nous tirons profit de techniques de réécriture de requêtes à l aide de vues afin de diffuser les données de ces abonnements, à partir d autres abonnements.Notre contribution principale est un nouvel algorithme qui organise les vues dans un réseau de dissémination d information multi-niveaux ; ce réseau est calculé à l aide d outils techniques de programmation linéaire afin de passer à l échelle pour de grands nombres de vues, respecter les contraintes de capacité du système, et minimiser les délais de propagation des information. L efficacité et la performance de notre algorithme est confirmée par notre évaluation expérimentale, qui inclut l étude d un déploiement réel dans un réseau WAN.XML was recommended by W3C in 1998 as a markup language to be used by device- and system-independent methods of representing information. XML is nowadays used as a data model for storing and querying large volumes of data in database systems. In spite of significant research and systems development, many performance problems are raised by processing very large amounts of XML data. Materialized views have long been used in databases to speed up queries. Materialized views can be seen as precomputed query results that can be re-used to evaluate (part of) another query, and have been a topic of intensive research, in particular in the context of relational data warehousing. This thesis investigates the applicability of materialized views techniques to optimize the performance of Web data management tools, in particular in distributed settings, considering XML data and queries. We make three contributions.We first consider the problem of choosing the best views to materialize within a given space budget in order to improve the performance of a query workload. Our work is the first to address the view selection problem for a rich subset of XQuery. The challenges we face stem from the expressive power and features of both the query and view languages and from the size of the search space of candidate views to materialize. While the general problem has prohibitive complexity, we propose and study a heuristic algorithm and demonstrate its superior performance compared to the state of the art.Second, we consider the management of large XML corpora in peer-to-peer networks, based on distributed hash tables (or DHTs, in short). We consider a platform leveraging distributed materialized XML views, defined by arbitrary XML queries, filled in with data published anywhere in the network, and exploited to efficiently answer queries issued by any network peer. This thesis has contributed important scalability oriented optimizations, as well as a comprehensive set of experiments deployed in a country-wide WAN. These experiments outgrow by orders of magnitude similar competitor systems in terms of data volumes and data dissemination throughput. Thus, they are the most advanced in understanding the performance behavior of DHT-based XML content management in real settings.Finally, we present a novel approach for scalable content-based publish/subscribe (pub/sub, in short) in the presence of constraints on the available computational resources of data publishers. We achieve scalability by off-loading subscriptions from the publisher, and leveraging view-based query rewriting to feed these subscriptions from the data accumulated in others. Our main contribution is a novel algorithm for organizing subscriptions in a multi-level dissemination network in order to serve large numbers of subscriptions, respect capacity constraints, and minimize latency. The efficiency and effectiveness of our algorithm are confirmed through extensive experiments and a large deployment in a WAN.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF