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.
Bose and Fegaras @cite_59 , argue to use XML fragments for efficiently supporting data exchange in P2P networks. In this proposal, XML fragments are interrelated, and each fragment is univocally identified by an . Authors also propose a fragmentation schema, called , which allows us to define the structure of fragments across the network. In turn, the structure of fragments can be exploited for data exchange and query optimization purposes. Bonifati @cite_1 also define an XML fragmentation framework for P2P networks, called (XP2P). In this proposal, XML fragments are obtained and identified via a single root-to-node path expression, and managed on a specific peer. In addition, to data management efficiency purposes, in @cite_1 authors associate two XPath-modeled path expressions to each fragment, namely and , respectively. Given an XML fragment @math , the first XPath expression identifies the root of the fragment @math from which @math has been originated; the second XPath expression instead identifies the root of a @math 's child XML fragment. These path expressions ensure the easily identification of fragments and their networked relationships.
@cite_1 @cite_59
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We address the problem of querying XML data over a P2P network. In P2P networks, the allowed kinds of queries are usually exact-match queries over file names. We discuss the extensions needed to deal with XML data and XPath queries. A single peer can hold a whole document or a partial complete fragment of the latter. Each XML fragment document is identified by a distinct path expression, which is encoded in a distributed hash table. Our framework differs from content-based routing mechanisms, biased towards finding the most relevant peers holding the data. We perform fragments placement and enable fragments lookup by solely exploiting few path expressions stored on each peer. By taking advantage of quasi-zero replication of global catalogs, our system supports fast full and partial XPath querying. To this purpose, we have extended the Chord simulator and performed an experimental evaluation of our approach. 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.