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.
In order to distribute a data warehouse, Noaman @cite_4 exploit a top-down strategy making use of horizontal fragmentation. @cite_4 , authors propose an algorithm for deriving horizontal fragments from the fact table based on input queries defined on all the dimensional tables. Finally, Wehrle @cite_25 propose distributing and querying a data warehouse by meaningfully exploiting the capabilities offered by a . @cite_25 , authors make use of derived horizontal fragmentation to split the target data warehouse and build the so-called , 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.
@cite_4 @cite_25
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Data warehousing is one of the major research topics of appliedside database investigators. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. Unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. This research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. The article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a “horizontal” fragmentation algorithm for the fact relation. Data warehouses store large volumes of data according to a multidimensional model with dimensions representing different axes of analysis. OLAP systems (online analytical processing) provide the ability to interactively explore the data warehouse. Rising volumes and complexity of data favor the use of more powerful distributed computing architectures. Computing grids in particular are built for decentralized management of heterogeneous distributed resources. Their lack of centralized control however conflicts with classic centralized data warehouse models. To take advantage of a computing grid infrastructure to operate a data warehouse, several problems need to be solved. First, the warehouse data must be uniquely identified and judiciously partitioned to allow efficient distribution, querying and exchange among the nodes of the grid. We propose a data model based on "chunks" as atomic entities of warehouse data that can be uniquely identified. We then build contiguous blocks of these chunks to obtain suitable fragments of the data warehouse. The fragments stored on each grid node must be indexed in a uniform way to effectively interact with existing grid services. Our indexing structure consists of a lattice structure mapping queries to warehouse fragments and a specialized spatial index structure formed by X-trees providing the information necessary for optimized query evaluation plans.