XML data sources are gaining popularity in the context of Business Intelligence and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing complex and heterogeneous data. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore, recent research efforts are focusing on horizontal fragmentation techniques, which are able to overcome the above limitations. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses. In this paper, we propose the use of the K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses. We complement our analytical contribution with a comprehensive experimental assessment where we compare the efficiency of our proposal against existing fragmentation algorithms.
Andrade @cite_53 propose applying fragmentation to an collection of XML documents. @cite_53 , authors adapt traditional fragmentation techniques to an XML document collection, and make use of the (TLC) algebra @cite_34 to this goal. Authors also experimentally evaluate these techniques and show that horizontal fragmentation provides the best performance. Gertz and Bremer @cite_62 introduce a distribution approach for XML repositories. They propose a fragmentation method and outline an allocation model for distributed XML fragments in a centralized architecture. @cite_62 , authors also define horizontal and vertical fragmentation for XML repositories. Here, fragments are defined on the basis of a , called , which is derived from @cite_54 . In more detail, fragments are obtained via applying an expression on a graph representing XML data, named (). Moreover, authors provide exclusion expressions that ensure fragment coherence and disjunction rigorously.
@cite_54 @cite_53 @cite_34 @cite_62
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Fragmentation techniques for XML data are gaining momentum within both distributed and centralized XML query engines and pose novel and unrecognized challenges to the community. Albeit not novel, and clearly inspired by the classical divide et impera principle, fragmentation for XML trees has been proved successful in boosting the querying performance, and in cutting down the memory requirements. However, fragmentation considered so far has been driven by semantics, i.e. built around query predicates. In this paper, we propose a novel fragmentation technique that founds on structural constraints of XML documents (size, tree-width, and tree-depth) and on special-purpose structure histograms able to meaningfully summarize XML documents. This allows us to predict bounding intervals of structural properties of output (XML) fragments for efficient query processing of distributed XML data. An experimental evaluation of our study confirms the effectiveness of our fragmentation methodology on some representative XML data sets. Data fragmentation offers various attractive alternatives to organizing and managing data, and presents interesting characteristics that may be exploited for efficient processing. XML, being inherently hierarchical and semi-structured, is an ideal candidate to reap the benefits offered by data fragmentation. However, fragmenting XML data and handling queries on fragmented XML are fraught with challenges: seamless XML fragmentation and processing models are required for deft handling of query execution on inter-connected and inter-related XML fragments, without the need of reconstructing the entire document in memory. Recent research has studied some of the challenges and has provided some insight on the data representation, and on the rather intuitive approaches for processing fragmented XML. In this paper, we provide a novel pipelined framework, called XFrag, for processing XQueries on XML fragments to achieve processing and memory efficiency. Moreover, we show that this model is suitable for low-bandwidth mobile environments by accounting for their intrinsic idiosyncrasies, without sacrificing accuracy and efficiency. We provide experimental results showing the memory savings achieved by our framework using the XMark benchmark. XML is increasingly used not only for data exchange but also to represent arbitrary data sources as virtual XML repositories. In many application scenarios, fragments of such repositories are distributed over the Web. However, design and query processing models for distributed XML data have not yet been studied in detail. The goal of this paper is to study the design and management of distributed XML repositories. Following the well-established concepts of vertical and horizontal data fragmentation schemes for relational databases, we introduce a flexible distribution design approach for XML repositories. We provide a comprehensive data allocation model with a particular focus on storage efficient index structures. These index structures encode global path information about XML fragment data at local sites and provide for an efficient, local evaluation of the most common types of global path and tree pattern queries. Finally, we describe the basic principles of a distributed query processing model based on the concept of index shipping. The data volume of XML repositories and the response time of query processing have become critical issues for many applications, especially for those in the Web. An interesting alternative to improve query processing performance consists in reducing the size of XML databases through fragmentation techniques. However, traditional fragmentation definitions do not directly apply to collections of XML documents. This work formalizes the fragmentation definition for collections of XML documents, and shows the performance of query processing over fragmented XML data. Our prototype, PartiX, exploits intra-query parallelism on top of XQuery-enabled sequential DBMS modules. We have analyzed several experimental settings, and our results showed a performance improvement of up to a 72 scale up factor against centralized databases.