Abstract
XML data sources are more and more gaining popularity in the context of a wide family of Business Intelligence (BI) and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing semi-structured and complex multidimensional data. As a consequence, many XML data warehouse models have been proposed during past years in order to handle hetero-geneity and complexity of multidimensional data in a way traditional relational data warehouse approaches fail to achieve. 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 the attention on fragmentation techniques, which are able to overcome the limitations above. Derived horizontal fragmentation is already used in relational data warehouses, and can definitely be adapted to the XML context. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses, and, with more emphasis, distributed data warehouse architectures. Inspired by this research challenge, in this paper we propose the use of K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses, and, at the same time, completely controlling and determining the number of originated fragments via adequately setting the parameter K. We complete our analytical contribution by means of a comprehensive experimental assessment where we compare the efficiency of our proposed XML data warehouse fragmentation technique against those of classical derived horizontal fragmentation algorithms adapted to XML data warehouses.
Highlights
Nowadays, XML has become a standard for representing complex business data [19], so that decision support processes that make use of XML data sourcesCopyright c 2009 Inderscience Enterprises Ltd.A
In [76], authors make use of derived horizontal fragmentation to split the target data warehouse and build the so-called block of chunks, which is a set of data portions derived from the data warehouse and used to query optimization purposes, being each portion computed as a fragment of the partition
We have introduced an approach for fragmenting XML data warehouses that is based on Data Mining, and, more precisely, on K -means clustering algorithm
Summary
XML has become a standard for representing complex business data [19], so that decision support processes that make use of XML data sources. Many efforts towards the so-called XML Data Warehousing have been achieved during the past few years [26, 72, 85], as well as efforts focused to extend XQuery [20] with near On-Line Analytical Processing (OLAP) [31, 43] capabilities such as advanced grouping and aggregation features [19, 59, 77] In this context, performance is a critical issue, as actual XML-native database systems (e.g., eXist [60], TIMBER [46], X-Hive [82], and Sedna [37]) suffer from limited performance, both in terms of volumes of manageable manageable data and response time to complex analytical queries.
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