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

Efficient processing of XML documents

Ph.DDOCTOR OF PHILOSOPH

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

筑波大学計算科学研究センター 平成21年度 年次報告書

1.平成21年度 基本方針、重点施策・改善目標等　……　42.平成21年度 実績報告　……　71.素粒子宇宙研究部門　……　131.1.素粒子分野　……　131.2.宇宙分野　……　262.物質生命研究部門　……　482.1.物質工学理論グループ　……　482.2.生命物理グループ　……　552.3.計算物性グループ　……　662.4.原子核理論グループ　……　803.地球生物環境研究部門　……　883.1.地球環境学分野　……　883.2.生物分野　……　964.超高速計算システム研究部門　……　1025.計算情報学研究部門　……　1105.1.計算知能分野　……　1105.2.計算メディア分野　……　12

XML Query optimization

Ph.DDOCTOR OF PHILOSOPH

An experimental study and evaluation of a new architecture for clinical decision support - integrating the openEHR specifications for the Electronic Health Record with Bayesian Networks

Healthcare informatics still lacks wide-scale adoption of intelligent decision support methods, despite continuous increases in computing power and methodological advances in scalable computation and machine learning, over recent decades. The potential has long been recognised, as evidenced in the literature of the domain, which is extensively reviewed. The thesis identifies and explores key barriers to adoption of clinical decision support, through computational experiments encompassing a number of technical platforms. Building on previous research, it implements and tests a novel platform architecture capable of processing and reasoning with clinical data. The key components of this platform are the now widely implemented openEHR electronic health record specifications and Bayesian Belief Networks. Substantial software implementations are used to explore the integration of these components, guided and supplemented by input from clinician experts and using clinical data models derived in hospital settings at Moorfields Eye Hospital. Data quality and quantity issues are highlighted. Insights thus gained are used to design and build a novel graph-based representation and processing model for the clinical data, based on the openEHR specifications. The approach can be implemented using diverse modern database and platform technologies. Computational experiments with the platform, using data from two clinical domains – a preliminary study with published thyroid metabolism data and a substantial study of cataract surgery – explore fundamental barriers that must be overcome in intelligent healthcare systems developments for clinical settings. These have often been neglected, or misunderstood as implementation procedures of secondary importance. The results confirm that the methods developed have the potential to overcome a number of these barriers. The findings lead to proposals for improvements to the openEHR specifications, in the context of machine learning applications, and in particular for integrating them with Bayesian Networks. The thesis concludes with a roadmap for future research, building on progress and findings to date

Structural Summaries as a Core Technology for Efficient XML Retrieval

The Extensible Markup Language (XML) is extremely popular as a generic markup language for text documents with an explicit hierarchical structure. The different types of XML data found in today’s document repositories, digital libraries, intranets and on the web range from flat text with little meaningful structure to be queried, over truly semistructured data with a rich and often irregular structure, to rather rigidly structured documents with little text that would also fit a relational database system (RDBS). Not surprisingly, various ways of storing and retrieving XML data have been investigated, including native XML systems, relational engines based on RDBSs, and hybrid combinations thereof. Over the years a number of native XML indexing techniques have emerged, the most important ones being structure indices and labelling schemes. Structure indices represent the document schema (i.e., the hierarchy of nested tags that occur in the documents) in a compact central data structure so that structural query constraints (e.g., path or tree patterns) can be efficiently matched without accessing the documents. Labelling schemes specify ways to assign unique identifiers, or labels, to the document nodes so that specific relations (e.g., parent/child) between individual nodes can be inferred from their labels alone in a decentralized manner, again without accessing the documents themselves. Since both structure indices and labelling schemes provide compact approximate views on the document structure, we collectively refer to them as structural summaries. This work presents new structural summaries that enable highly efficient and scalable XML retrieval in native, relational and hybrid systems. The key contribution of our approach is threefold. (1) We introduce BIRD, a very efficient and expressive labelling scheme for XML, and the CADG, a combined text and structure index, and combine them as two complementary building blocks of the same XML retrieval system. (2) We propose a purely relational variant of BIRD and the CADG, called RCADG, that is extremely fast and scales up to large document collections. (3) We present the RCADG Cache, a hybrid system that enhances the RCADG with incremental query evaluation based on cached results of earlier queries. The RCADG Cache exploits schema information in the RCADG to detect cached query results that can supply some or all matches to a new query with little or no computational and I/O effort. A main-memory cache index ensures that reusable query results are quickly retrieved even in a huge cache. Our work shows that structural summaries significantly improve the efficiency and scalability of XML retrieval systems in several ways. Former relational approaches have largely ignored structural summaries. The RCADG shows that these native indexing techniques are equally effective for XML retrieval in RDBSs. BIRD, unlike some other labelling schemes, achieves high retrieval performance with a fairly modest storage overhead. To the best of our knowledge, the RCADG Cache is the only approach to take advantage of structural summaries for effectively detecting query containment or overlap. Moreover, no other XML cache we know of exploits intermediate results that are produced as a by-product during the evaluation from scratch. These are valuable cache contents that increase the effectiveness of the cache at no extra computational cost. Extensive experiments quantify the practical benefit of all of the proposed techniques, which amounts to a performance gain of several orders of magnitude compared to various other approaches