Automaton Meet Algebra: A Hybrid Paradigm for Efficiently Processing XQuery over XML Stream

XML stream applications bring the challenge of efficiently processing queries on sequentially accessible token-based data streams. The automaton paradigm is naturally suited for pattern retrieval on tokenized XML streams, but requires patches for implementing the filtering or restructuring functionalities common for the XML query languages. In contrast, the algebraic paradigm is well-established for processing self-contained tuples. However, it does not traditionally support token inputs. This dissertation proposes a framework called Raindrop, which accommodates both the automaton and algebra paradigms to take advantage of both. First, we propose an architecture for Raindrop. Raindrop is an algebra framework that models queries at different abstraction levels. We represent the token-based automaton computations as an algebraic subplan at the high level while exposing the automaton details at the low level. The algebraic subplan modeling automaton computations can thus be integrated with the algebraic subplan modeling the non-automaton computations. Second, we explore a novel optimization opportunity. Other XML stream processing systems always retrieve all the patterns in a query in the automaton. In contrast, Raindrop allows a plan to retrieve some of the pattern retrieval in the automaton and some out of the automaton. This opens up an automaton-in-or-out optimization opportunity. We study this optimization in two types of run-time environments, one with stable data characteristics and one with fluctuating data characteristics. We provide search strategies catering to each environment. We also describe how to migrate from a currently running plan to a new plan at run-time. Third, we optimize the automaton computations using the schema knowledge. A set of criteria are established to decide what schema constraints are useful to a given query. Optimization rules utilizing different types of schema constraints are proposed based on the criteria. We design a rule application algorithm which ensures both completeness (i.e., no optimization is missed) and minimality (i.e., no redundant optimization is introduced). The experimentations on both real and synthetic data illustrate that these techniques bring significant performance improvement with little overhead

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Progressive Query Processing

Ph.DDOCTOR OF PHILOSOPH

Enhancement of Query processing on XML data

Ph.DDOCTOR OF PHILOSOPH

Compressed self-indexed XML representation with efficient XPath evaluation

[Abstract] The popularity of the eXtensible Markup Language (XML) has been continuously growing since its first introduction, being today acknowledged as the de facto standard for semi-structured data representation and data exchange on the World Wide Web. In this scenario, several query languages were proposed to exploit the expressiveness of XML data, as well as systems to provide an eficient support. At the same time, as research in compression became more and more relevant, works also focused their efforts on studying new approaches to provide eficient solutions, using the minimum amount of space. Today, however, there is a lack of practical available tools that join both eficient query support, and minimum space requirements. In this thesis we address this problem, and propose a new approach for storing, processing and querying XML documents in time and space eficient way, by specially focusing on XPath queries. We have developed a new compressed selfindexed representation of XML documents that obtains compression ratios about 30%-40%, over which a query module providing eficient XPath query evaluation has also been developed. As a whole, both parts make up a complete system, we called XXS, for the eficient evaluation of XPath queries over compressed self-indexed XML documents. Experimental results show the outstanding performance of our proposal, which can successfully compete with some of the best-known solutions, and that largely outperforms them in terms of space.[Resumo] A popularidade do eXtensible Markup Language (XML) non fixo máis que medrar dende a súa introdución inicial, sendo recoñecido hoxe en día como o estándar de facto para a representación de datos semi-estruturados e o intercambio de datos na Rede. Baixo este escenario, son varias as linguaxes de consulta que se propuxeron para explotar a expresividade dos datos en formato XML, así como sistemas que proporcionasen un soporte eficiente a eles. Ó mesmo tempo, e conforme a investigación en compresión se fixo cada vez máis relevante, os esforzos tamén foron dirixidos a estudiar novas aproximacións que ofrecesen solucións eficientes, pero usando ademáis a menor cantidade de espacio posible. Actualmente, sen embargo, existe unha clara ausencia de ferramentas prácticas dispoñibles que agrupen ambas características: un soporte á realización de consultas eficiente, xunto con requisitos de espacio mínimos. Nesta tese abordamos ese problema, e propoñemos unha nova solución para o almacenamento, procesamento e consulta de documentos XML, eficiente tanto en tempo como en espacio, centrándonos, en particular, na linguaxe de consulta XPath. Así, desenvolvimos unha nova representación comprimida e auto-indexada de documentos XML, que obtén ratios de compresión en torno ó 30%-40%, e sobre a cal se creou tamén un módulo de consulta para a eficiente evaluación de consultas XPath. En conxunto, ambas contribucións conforman un sistema completo, que chamamos XXS, para a evaluación eficiente de consultas XPath sobre documentos XML comprimidos e auto-indexados. Os resultados experimentais amosan o destacado comportamento da nosa ferramenta, que é capaz de competir exitosamente con algunhas das solucións máis coñecidas, ás que ademáis supera claramente en termos de espacio.[Resumen] La popularidad del eXtensible Markup Language (XML) no ha hecho sino más que ir en aumento desde su introducción inicial, siendo hoy día reconocido como el estándar de facto para la representación de datos semi-estructurados, y el intercambio de datos en Internet. Bajo este escenario, son varios los lenguajes de consulta que se han venido proponiendo para explotar la expresividad de los datos en formato XML, así como sistemas que proporcionasen un soporte eficiente a ellos. Al mismo tiempo, y conforme la investigación en compresión se ha hecho cada vez más relevante, los esfuerzos se han dirigido también a estudiar nuevas aproximaciones que ofreciesen soluciones eficientes, pero usando además la menor cantidad de espacio posible. Actualmente, sin embargo, existe una clara ausencia de herramientas prácticas disponibles que aúnen ambas características: un soporte a la realización de consultas eficiente, con requisitos de espacio mínimos. En esta tesis abordamos ese problema, y proponemos una nueva solución para el almacenamiento, procesamiento y consulta de documentos XML, eficiente en tiempo y en espacio, centrándonos, en particular, en el lenguaje de consulta XPath. Así, hemos desarrollado una nueva representación comprimida y auto-indexada de documentos XML, que obtiene ratios de compresión del 30%-40%, y sobre la cual se ha creado un módulo de consulta para la eficiente evaluación de consultas XPath. En conjunto, ambas contribuciones conforman un sistema completo, que hemos dado en llamar XXS, para la evaluación eficiente de consultas XPath sobre documentos XML comprimidos y auto-indexados. Los resultados experimentales evidencian el destacado comportamiento de nuestra herramienta, que es capaz de competir exitosamente con algunas de las soluciones más conocidas, a las que además supera claramente en términos de espacio

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

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Keyword-Based Querying for the Social Semantic Web

Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches

Child Prime Label Approaches to Evaluate XML Structured Queries

The adoption of the eXtensible Markup Language (XML) as the standard format to store and exchange semi-structure data has been gaining momentum. The growing number of XML documents leads to the need for appropriate XML querying algorithms which are able to retrieve XML data efficiently. Due to the importance of twig pattern matching in XML retrieval systems, finding all matching occurrences of a tree pattern query in an XML document is often considered as a specific task for XML databases as well as a core operation in XML query processing. This thesis presents a design and implementation of a new indexing technique, called the Child Prime Label (CPL) which exploits the property of prime numbers to identify Parent-Child (P-C) edges in twig pattern queries (TPQs) during query evaluation. The CPL approach can be incorporated efficiently within the existing labelling schemes. The major contributions of this thesis can be seen as a set of novel twig matching algorithms which apply the CPL approach and focus on reducing the overhead of storing useless elements and performing unnecessary computations during the output enumeration. The research presented here is the first to provide an efficient and general solution for TPQs containing ordering constraints and positional predicates specified by the XML query languages. To evaluate the CPL approaches, the holistic model was implemented as an experimental prototype in which the approaches proposed are compared against state-of-the-art holistic twig algorithms. Extensive performance studies on various real-world and artificial datasets were conducted to demonstrate the significant improvement of the CPL approaches over the previous indexing and querying methods. The experimental results demonstrate the validity and improvements of the new algorithms over other related methods on common various subclasses of TPQs. Moreover, the scalability tests reveal that the new algorithms are more suitable for processing large XML datasets

Proceedings

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 268 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891