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.
 Within the framework of the data warehouse design methodology we are developing, in this paper we investigate the problem of vertical fragmentation of relational views aimed at minimizing the global query response time. Each view includes several measures which, within the workload, are seldom requested together; thus, the system performance may be increased by partitioning the views to be materialized into smaller tables. On the other hand, drill-across queries involve measures taken from two or more views; in this case the access costs may be decreased by unifying these views into larger tables. Within the data warehouse context, the presence of redundant views makes the fragmentation problem more complex than in traditional relational databases since it requires to decide on which views each query should be executed. After formalizing the fragmentation problem as a 0-1 integer linear programming problem, we define a cost function and propose a branch-and-bound algorithm to minimize it. Finally, we demonstrate the usefulness of our approach by presenting a sample set of experimental results. Overview Recently, there has been a great deal of discussion in the trade press and elsewhere regarding the coexistence of so-called transaction databases with decision support systems. These discussions usually revolve around the argument that the physical design required for acceptable performance of each is incompatible and that therefore, data should be stored redundantly in multiple enterprise databases: one for transaction processing, and the other for decision support type activities. Also, these same arguments usually confuse physical schema with logical and conceptual schema. Data analysis applications typically aggregate data across many dimensions looking for unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional answers. Applications need the N-dimensional generalization of these operators. The paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers. The cube treats each of the N aggregation attributes as a dimension of N-space. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an N-dimensionaI cube. Super-aggregates are computed by aggregating the N-cube to lower dimensional spaces. Aggregation points are represented by an "infinite value": ALL, so the point (ALL,ALL,...,ALL, sum(*)) represents the global sum of all items. Each ALL value actually represents the set of values contributing to that aggregation. Data warehousing and On-Line Analytical Processing (OLAP) are becoming critical components of decision support as advances in technology are improving the ability to manage and retrieve large volumes of data. Data warehousing refers to collection of decision support technologies aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions" [1]. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. It is often used by management analysts and decision makers in a variety of functional areas such as sales and marketing planning. Typically, OLAP queries look for speci c trends and anomalies in the base information by aggregating, ranging, ltering and grouping data in many di erent ways [8]. E cient query processing is a critical requirement for OLAP because the underlying data warehouse is very large, queries are often quite complex, and decision support applications typically require in-