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

Answering Tag-Term Keyword Queries over XML Documents in DHT Networks

Abstract. The emergence of Peer-to-Peer (P2P) computing model and the popularity of Extensible Markup Language (XML) as the web data format have fueled the extensive research on retrieving XML data in P2P networks. In this paper, we developed an efficient and effective keyword search framework that can support tag-term keyword queries in Distributed Hash Table (DHT) networks. We employed a concise Bloom-Filter data structure to index XML meta-data in the DHT repository. We also developed an effective algorithm that supports tag-term keyword queries over our Bloom-Filter encoded XML meta-data in the DHT network. We conducted extensive experiments to demonstrate the efficiency of indexing scheme, the effectiveness of our keyword query algorithm and the system scalability of our framework

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

A study of gossip algorithms for internet-scale cardinality estimation of distributed XML data

Title from PDF of title page, viewed on October 3, 2012Thesis advisor: Praveen R. RaoVitaIncludes bibliographic references (p. 58-69)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2012After more than a decade of active research and development, the peer-to-peer (P2P) computing model continues to be successful. We have witnessed the deployment of commercial P2P applications in large, Internet-scale environments. With the rise and growth of P2P, indexing and querying data stored in large-scale sharing systems has become increasingly di cult. Computing statistics over data stored in Internet-scale P2P systems is an important component of query optimization. Decentralized gossip-based protocols are very popular in networking, and in particular, in sensor networks. The simplicity and scalability of gossip protocols render them perfect for quickly computing accurate estimates of aggregates (sums, averages, etc.) in Internet-scale systems where node and link failures are the norm. In this thesis, we present the problem of cardinality estimation of XPath queries over XML data stored in a distributed, Internet-scale environment. We focus our work on three objectives: implementing gossip in an Internet-scale environment, conducting a comprehensive performance evaluation in a wide-area network, and analyzing the experimental results. We implement two gossip-based algorithms (VanillaXGossip and XGossip) which, given an XPath query, estimate the number of XML documents in the network that contain a match for the query. XGossip employs a new, divide-and-conquer strategy for load-balancing and reducing the bandwidth consumption. We conduct a comprehensive performance evaluation of both gossip algorithms on Amazon Elastic Compute Cloud (Amazon EC2) web service using a heterogeneous collection of XML documents. The goal of the performance evaluation is to nd if the results we obtain are consistent with the theoretical analysis of VanillaXGossip and XGossip.Introduction -- Background and motivations -- The design of VanillaXGossip and XGossip -- Implementation of VanillaXGossip and XGossip -- Evaluation -- Conclusion and future work -- Appendix A. Algorithms -- Appendix B. XPath gramma

Data replication and update propagation in XML P2P data management systems

XML P2P data management systems are P2P systems that use XML as the underlying data format shared between peers in the network. These systems aim to bring the benefits of XML and P2P systems to the distributed data management field. However, P2P systems are known for their lack of central control and high degree of autonomy. Peers may leave the network at any time at will, increasing the risk of data loss. Despite this, most research in XML P2P systems focus on novel and efficient XML indexing and retrieval techniques. Mechanisms for ensuring data availability in XML P2P systems has received comparatively little attention. This project attempts to address this issue. We design an XML P2P data management framework to improve data availability. This framework includes mechanisms for wide-spread data replication, replica location and update propagation. It allows XML documents to be broken down into fragments. By doing so, we aim to reduce the cost of replicating data by distributing smaller XML fragments throughout the network rather than entire documents. To tackle the data replication problem, we propose a suite of selection and placement algorithms that may be interchanged to form a particular replication strategy. To support the placement of replicas anywhere in the network, we use a Fragment Location Catalogue, a global index that maintains the locations of replicas. We also propose a lazy update propagation algorithm to propagate updates to replicas. Experiments show that the data replication algorithms improve data availability in our experimental network environment. We also find that breaking XML documents into smaller pieces and replicating those instead of whole XML documents considerably reduces the replication cost, but at the price of some loss in data availability. For the update propagation tests, we find that the probability that queries return up-to-date results increases, but improvements to the algorithm are necessary to handle environments with high update rates