DescribeX: A Framework for Exploring and Querying XML Web Collections

This thesis introduces DescribeX, a powerful framework that is capable of describing arbitrarily complex XML summaries of web collections, providing support for more efficient evaluation of XPath workloads. DescribeX permits the declarative description of document structure using all axes and language constructs in XPath, and generalizes many of the XML indexing and summarization approaches in the literature. DescribeX supports the construction of heterogeneous summaries where different document elements sharing a common structure can be declaratively defined and refined by means of path regular expressions on axes, or axis path regular expression (AxPREs). DescribeX can significantly help in the understanding of both the structure of complex, heterogeneous XML collections and the behaviour of XPath queries evaluated on them. Experimental results demonstrate the scalability of DescribeX summary refinements and stabilizations (the key enablers for tailoring summaries) with multi-gigabyte web collections. A comparative study suggests that using a DescribeX summary created from a given workload can produce query evaluation times orders of magnitude better than using existing summaries. DescribeX's light-weight approach of combining summaries with a file-at-a-time XPath processor can be a very competitive alternative, in terms of performance, to conventional fully-fledged XML query engines that provide DB-like functionality such as security, transaction processing, and native storage.Comment: PhD thesis, University of Toronto, 2008, 163 page

XML query processing: Indices and histograms

Ph.DDOCTOR OF PHILOSOPH

Evaluation and selectivity estimation of XML queries

Ph.DDOCTOR OF PHILOSOPH

The relational XQuery puzzle: a look-back on the pieces found so far

Given the tremendous versatility of relational database implementations toward awide range of database problems, it seems only natural to consider them as back-ends for XML data processing. Yet, the assumptions behind the language XQuery are considerably different to those in traditional RDBMSs. The underlying data model is a tree, data and results carry an intrinsic order, queries are described using explicit iteration and, after all, problems are everything else but regular. Solving the relational XQuery puzzle, therefore, has challenged anumber of research groups over the past years. The purpose of this article is to summarize and assess some of the results that have been obtained during this period to solve the puzzle. Our main focus is on the Pathfinder XQuery compiler, afull reference implementation of apurely relational XQuery processor. As we dissect its components, we relate them to other work in the field and also point to open problems and limitations in the context of relational XQuery processin

Algorithms for XML stream processing : massive data, external memory and scalable performance

Many modern applications require processing of massive streams of XML data, creating difficult technical challenges. Among these, there is the design and implementation of applications to optimize the processing of XPath queries and to provide an accurate cost estimation for these queries processed on a massive steam of XML data. In this thesis, we propose a novel performance prediction model which a priori estimates the cost (in terms of space used and time spent) for any structural query belonging to Forward XPath. In doing so, we perform an experimental study to confirm the linear relationship between stream-processing and data-access resources. Therefore, we introduce a mathematical model (linear regression functions) to predict the cost for a given XPath query. Moreover, we introduce a new selectivity estimation technique. It consists of two elements. The first one is the path tree structure synopsis: a concise, accurate, and convenient summary of the structure of an XML document. The second one is the selectivity estimation algorithm: an efficient streamquerying algorithm to traverse the path tree synopsis for estimating the values of cost-parameters. Those parameters are used by the mathematical model to determine the cost of a given XPath query. We compare the performance of our model with existing approaches. Furthermore, we present a use case for an online stream-querying system. The system uses our performance predicate model to estimate the cost for a given XPath query in terms of time/memory. Moreover, it provides an accurate answer for the query's sender. This use case illustrates the practical advantages of performance management with our techniques.Plusieurs applications modernes nécessitent un traitement de flux massifs de données XML, cela crée de défis techniques. Parmi ces derniers, il y a la conception et la mise en ouvre d'outils pour optimiser le traitement des requêtes XPath et fournir une estimation précise des coûts de ces requêtes traitées sur un flux massif de données XML. Dans cette thèse, nous proposons un nouveau modèle de prédiction de performance qui estime a priori le coût (en termes d'espace utilisé et de temps écoulé) pour les requêtes structurelles de Forward XPath. Ce faisant, nous réalisons une étude expérimentale pour confirmer la relation linéaire entre le traitement de flux, et les ressources d'accès aux données. Par conséquent, nous présentons un modèle mathématique (fonctions de régression linéaire) pour prévoir le coût d'une requête XPath donnée. En outre, nous présentons une technique nouvelle d'estimation de sélectivité. Elle se compose de deux éléments. Le premier est le résumé path tree: une présentation concise et précise de la structure d'un document XML. Le second est l'algorithme d'estimation de sélectivité: un algorithme efficace de flux pour traverser le synopsis path tree pour estimer les valeurs des paramètres de coût. Ces paramètres sont utilisés par le modèle mathématique pour déterminer le coût d'une requête XPath donnée. Nous comparons les performances de notre modèle avec les approches existantes. De plus, nous présentons un cas d'utilisation d'un système en ligne appelé "online stream-querying system". Le système utilise notre modèle de prédiction de performance pour estimer le coût (en termes de temps / mémoire) d'une requête XPath donnée. En outre, il fournit une réponse précise à l'auteur de la requête. Ce cas d'utilisation illustre les avantages pratiques de gestion de performance avec nos technique