A Linear-Time Algorithm for Optimal Tree Sibling Partitioning and its Application to XML Data Stores

Document insertion into a native XML Data Store (XDS) requires to partition the document tree into a number of storage units with limited capacity, such as records on disk pages. As intra partition navigation is much faster than navigation between partitions, minimizing the number of partitions has a beneficial effect on query performance. We present a linear time algorithm to optimally partition an ordered, labeled, weighted tree such that each partition does not exceed a fixed weight limit. Whereas traditionally tree partitioning algorithms only allow child nodes to share a partition with their parent node (i.e. a partition corresponds to a subtree), our algorithm also considers partitions containing several subtrees as long as their roots are adjacent siblings. We call this sibling partitioning. Based on our study of the optimal algorithm, we further introduce two novel, near-optimal heuristics. They are easier to implement, do not need to hold the whole document instance in memory, and require much less runtime than the optimal algorithm. Finally, we provide an experimental study comparing our novel and existing algorithms. One important finding is that compared to partitioning that exclusively considers parent-child partitions, including sibling partitioning as well can decrease the total number of partitions by more than 90%, and improve query performance by more than a factor of two

An Algebraic Approach to XQuery Optimization

As more data is stored in XML and more applications need to process this data, XML query optimization becomes performance critical. While optimization techniques for relational databases have been developed over the last thirty years, the optimization of XML queries poses new challenges. Query optimizers for XQuery, the standard query language for XML data, need to consider both document order and sequence order. Nevertheless, algebraic optimization proved powerful in query optimizers in relational and object oriented databases. Thus, this dissertation presents an algebraic approach to XQuery optimization. In this thesis, an algebra over sequences is presented that allows for a simple translation of XQuery into this algebra. The formal definitions of the operators in this algebra allow us to reason formally about algebraic optimizations. This thesis leverages the power of this formalism when unnesting nested XQuery expressions. In almost all cases unnesting nested queries in XQuery reduces query execution times from hours to seconds or milliseconds. Moreover, this dissertation presents three basic algebraic patterns of nested queries. For every basic pattern a decision tree is developed to select the most effective unnesting equivalence for a given query. Query unnesting extends the search space that can be considered during cost-based optimization of XQuery. As a result, substantially more efficient query execution plans may be detected. This thesis presents two more important cases where the number of plan alternatives leads to substantially shorter query execution times: join ordering and reordering location steps in path expressions. Our algebraic framework detects cases where document order or sequence order is destroyed. However, state-of-the-art techniques for order optimization in cost-based query optimizers have efficient mechanisms to repair order in these cases. The results obtained for query unnesting and cost-based optimization of XQuery underline the need for an algebraic approach to XQuery optimization for efficient XML query processing. Moreover, they are applicable to optimization in relational databases where order semantics are considered

Distributed XML Query Processing

While centralized query processing over collections of XML data stored at a single site is a well understood problem, centralized query evaluation techniques are inherently limited in their scalability when presented with large collections (or a single, large document) and heavy query workloads. In the context of relational query processing, similar scalability challenges have been overcome by partitioning data collections, distributing them across the sites of a distributed system, and then evaluating queries in a distributed fashion, usually in a way that ensures locality between (sub-)queries and their relevant data. This thesis presents a suite of query evaluation techniques for XML data that follow a similar approach to address the scalability problems encountered by XML query evaluation. Due to the significant differences in data and query models between relational and XML query processing, it is not possible to directly apply distributed query evaluation techniques designed for relational data to the XML scenario. Instead, new distributed query evaluation techniques need to be developed. Thus, in this thesis, an end-to-end solution to the scalability problems encountered by XML query processing is proposed. Based on a data partitioning model that supports both horizontal and vertical fragmentation steps (or any combination of the two), XML collections are fragmented and distributed across the sites of a distributed system. Then, a suite of distributed query evaluation strategies is proposed. These query evaluation techniques ensure locality between each fragment of the collection and the parts of the query corresponding to the data in this fragment. Special attention is paid to scalability and query performance, which is achieved by ensuring a high degree of parallelism during distributed query evaluation and by avoiding access to irrelevant portions of the data. For maximum flexibility, the suite of distributed query evaluation techniques proposed in this thesis provides several alternative approaches for evaluating a given query over a given distributed collection. Thus, to achieve the best performance, it is necessary to predict and compare the expected performance of each of these alternatives. In this work, this is accomplished through a query optimization technique based on a distribution-aware cost model. The same cost model is also used to fine-tune the way a collection is fragmented to the demands of the query workload evaluated over this collection. To evaluate the performance impact of the distributed query evaluation techniques proposed in this thesis, the techniques were implemented within a production-quality XML database system. Based on this implementation, a thorough experimental evaluation was performed. The results of this evaluation confirm that the distributed query evaluation techniques introduced here lead to significant improvements in query performance and scalability both when compared to centralized techniques and when compared to existing distributed query evaluation techniques

Enhancement of Query processing on XML data

Ph.DDOCTOR OF PHILOSOPH

Implementation of Web Query Languages Reconsidered

Visions of the next generation Web such as the "Semantic Web" or the "Web 2.0" have triggered the emergence of a multitude of data formats. These formats have different characteristics as far as the shape of data is concerned (for example tree- vs. graph-shaped). They are accompanied by a puzzlingly large number of query languages each limited to one data format. Thus, a key feature of the Web, namely to make it possible to access anything published by anyone, is compromised. This thesis is devoted to versatile query languages capable of accessing data in a variety of Web formats. The issue is addressed from three angles: language design, common, yet uniform semantics, and common, yet uniform evaluation. % Thus it is divided in three parts: First, we consider the query language Xcerpt as an example of the advocated class of versatile Web query languages. Using this concrete exemplar allows us to clarify and discuss the vision of versatility in detail. Second, a number of query languages, XPath, XQuery, SPARQL, and Xcerpt, are translated into a common intermediary language, CIQLog. This language has a purely logical semantics, which makes it easily amenable to optimizations. As a side effect, this provides the, to the best of our knowledge, first logical semantics for XQuery and SPARQL. It is a very useful tool for understanding the commonalities and differences of the considered languages. Third, the intermediate logical language is translated into a query algebra, CIQCAG. The core feature of CIQCAG is that it scales from tree- to graph-shaped data and queries without efficiency losses when tree-data and -queries are considered: it is shown that, in these cases, optimal complexities are achieved. CIQCAG is also shown to evaluate each of the aforementioned query languages with a complexity at least as good as the best known evaluation methods so far. For example, navigational XPath is evaluated with space complexity O(q d) and time complexity O(q n) where q is the query size, n the data size, and d the depth of the (tree-shaped) data. CIQCAG is further shown to provide linear time and space evaluation of tree-shaped queries for a larger class of graph-shaped data than any method previously proposed. This larger class of graph-shaped data, called continuous-image graphs, short CIGs, is introduced for the first time in this thesis. A (directed) graph is a CIG if its nodes can be totally ordered in such a manner that, for this order, the children of any node form a continuous interval. CIQCAG achieves these properties by employing a novel data structure, called sequence map, that allows an efficient evaluation of tree-shaped queries, or of tree-shaped cores of graph-shaped queries on any graph-shaped data. While being ideally suited to trees and CIGs, the data structure gracefully degrades to unrestricted graphs. It yields a remarkably efficient evaluation on graph-shaped data that only a few edges prevent from being trees or CIGs

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

Automatic Physical Design for XML Databases

Database systems employ physical structures such as indexes and materialized views to improve query performance, potentially by orders of magnitude. It is therefore important for a database administrator to choose the appropriate configuration of these physical structures (i.e., the appropriate physical design) for a given database. Deciding on the physical design of a database is not an easy task, and a considerable amount of research exists on automatic physical design tools for relational databases. Recently, XML database systems are increasingly being used for managing highly structured XML data, and support for XML data is being added to commercial relational database systems. This raises the important question of how to choose the appropriate physical design (i.e., the appropriate set of physical structures) for an XML database. Relational automatic physical design tools are not adequate, so new research is needed in this area. In this thesis, we address the problem of automatic physical design for XML databases, which is the process of automatically selecting the best set of physical structures for a given database and a given query workload representing the client application's usage patterns of this data. We focus on recommending two types of physical structures: XML indexes and relational materialized views of XML data. For each of these structures, we study the recommendation process and present a design advisor that automatically recommends a configuration of physical structures given an XML database and a workload of XML queries. The recommendation process is divided into four main phases: (1) enumerating candidate physical structures, (2) generalizing candidate structures in order to generate more candidates that are useful to queries that are not seen in the given workload but similar to the workload queries, (3) estimating the benefit of various candidate structures, and (4) selecting the best set of candidate structures for the given database and workload. We present a design advisor for recommending XML indexes, one for recommending materialized views, and an integrated design advisor that recommends both indexes and materialized views. A key characteristic of our advisors is that they are tightly coupled with the query optimizer of the database system, and rely on the optimizer for enumerating and evaluating physical designs whenever possible. This characteristic makes our techniques suitable for any database system that complies with a set of minimum requirements listed within the thesis. We have implemented the index, materialized view, and integrated advisors in a prototype version of IBM DB2 V9, which supports both relational and XML data, and we experimentally demonstrate the effectiveness of their recommendations using this implementation

Uma abordagem para o particionamento de dados na nuvem baseada em relações de afinidade em grafos

Orientadora : Profª. Drª. Carmem Satie HaraTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 21/07/2014Inclui referênciasResumo: Os desafios atuais do gerenciamento de dados vêm sendo frequentemente associados ao termo Big Data. Este termo refere-se a um número crescente de aplicações caracterizadas pela produção de dados com alta variedade, grande volume, e que exigem velocidade em seu processamento. Ao mesmo tempo em que estes requisitos são identificados, o amadurecimento tecnológico associado à computação em nuvem alavancou uma mudança nos aspectos operacionais e econômicos da computação, sobretudo através de infraestruturas para o desenvolvimento de serviços escaláveis. A iniciativa do gerenciamento de dados sobre estas plataformas mostra-se adequada para tratar os desafios do Big Data através de um serviço de banco de dados em nuvem (Database as a Service). Uma forma de escalar aplicações que processam uma quantidade massiva de informações e através da fragmentação de grandes conjuntos de dados alocados sobre servidores de um sistema em nuvem. O principal problema associado a esta abordagem esta em particionar os dados de forma que consultas possam ser preferencialmente executadas de forma local para evitar o custo da troca de mensagens entre servidores. Em conjunto com este problema, a variedade de dados e o volume crescente associado as bases de dados em nuvem desafiam as soluções tradicionais para o particionamento de dados. Esta tese propõe um novo método para o particionamento de dados que tem como objetivo promover a escalabilidade de repositórios em nuvem. Para minimizar o custo da execução de consultas distribuídas, heurísticas sobre informações de carga de trabalho são utilizadas para identificar afinidades entre dados e estabelecer o agrupamento de itens fortemente relacionados em um mesmo servidor. O problema do particionamento e tratado pelos processos de fragmentação e alocação. O processo de fragmentação define unidades de armazenamento que contem itens de dados fortemente relacionados. Na fase seguinte, o processo de alocação utiliza o mesmo critério de agrupamento para co-alocar fragmentos nos servidores do repositório. A replicação é utilizada para maximizar a quantidade de dados relacionados em um mesmo servidor, porém, a quantidade de replicas gerada é controlada por todo o processo. A metodologia proposta esta focada em modelos em grafo estabelecidos pelos formatos RDF e XML, e que permitem representar uma variedade de outros modelos. A principal contribuição desta tese esta em definir o particionamento sobre uma visão sumarizada de um banco de dados similar a um esquema de banco de dados. Além de evitar a exaustão do processo de particionamento sobre grandes bases, esta solução permite reaplicar a estratégia obtida sobre novas porções de dados que estejam de acordo com o esquema e a carga de trabalho assumidos pelo processo. Esta metodologia se mostra adequada para acomodar o volume crescente de dados associado a repositórios em nuvem. Resultados experimentais mostram que a solução proposta é efetiva para melhorar o desempenho de consultas, se comparada a abordagens alternativas que tratam o mesmo problema.Abstract: The new challenges in data management have been referred to as Big Data. This term is related to an increasing number of applications characterized by generating data with a variety of types, huge volume, and by requiring high velocity processing. At the same time, cloud computing technologies are transforming the operational and economic aspects of computing, mainly due to the introduction of infrastructures to deploy scalable services. Cloud platforms have been properly applied to support data management and address Big Data challenges through a database service in the cloud (DaaS - Database as a Service). One approach to scale applications that process massive amounts of information is to fragment huge datasets and allocate them across distributed data servers. In this context, the main problem is to apply a partitioning schema that maximizes local query processing and avoids the cost of message passing among servers. Besides this problem, data variety and the ever-increasing volume of cloud datastores pose new challenges to traditional partitioning approaches. This thesis provides a new partitioning approach to scale query processing on cloud datastores. In order to minimize the cost of distributed queries, we apply heuristics based on workload data to identify the affinity among data items and cluster the most correlated data in the same server. We tackle the data partitioning problem as a twofold problem. First, data fragmentation defines storage units with strongly correlated items. Further, data allocation aims to collocate fragments that share correlated items. Data replication is applied to cluster related data as much as possible. However, data redundancy is controlled throughout the process. We focus on graph models given by the RDF and XML formats in order to support data variety. Our main contribution is a partitioning strategy defined over a summarized view of the dataset given as a database schema. The result of the process consists of a set of partitioning templates, which can be used to partition an existing dataset, as well as maintain the partitioning process when new data that conform to the schema and the workload are inserted to the dataset. This approach is suitable to deal with the increasing volume of data related to cloud datastores. Experimental results show that the proposed solution is effective for improving the query performance in cloud datastores, compared to related approaches