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 summary, the above-outlined proposals generally exploit derived horizontal fragmentation to reduce irrelevant data accesses and efficiently process join operations across multiple relations @cite_18 @cite_4 @cite_25 . From active literature @cite_51 , we also recognize that, in order to implement derived horizontal fragmentation of data warehouses, the outlined approaches prevalently make use of the following two main fragmentation methods:
@cite_51 @cite_18 @cite_4 @cite_25
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A new approach to vertical fragmentation in relational databases is proposed using association rules, a data-mining technique. Vertical fragmentation can enhance the performance of database systems by reducing the number of disk accesses needed by transactions. By adapting Apriori algorithm, a design methodology for vertical partitioning is proposed. The heuristic methodology is tested using two real-life databases for various minimum support levels and minimum confidence levels. In the smaller database, the partitioning solution obtained matched the optimal solution using exhaustive enumeration. The application of our method on the larger database resulted in the partitioning solution that has an improvement of 41.05 over unpartitioned solution and took less than a second to produce the solution. We provide future research directions on extending the procedure to distributed and object-oriented database designs. 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. In this paper, two-phase horizontal partitioning of distributed databases is addressed. First, primary horizontal fragmentation is carried out on each relation based on the predicate affinity matrix and the bond energy algorithm. This is an application of a vertical partitioning algorithm to the horizontal fragmentation problem. Second, the derived horizontal fragmentation is further performed by considering information related to the global relational database schema and its transactions. A necessary and sufficient condition for the correctness of derived fragmentations is also proved. Vertical and horizontal fragmentations are central issues in the design process of distributed object based systems. A good fragmentation scheme followed by an optimal allocation could greatly enhance performance in such systems, as data transfer between distributed sites is minimized. In this paper we present a horizontal fragmentation approach that uses the k-means AI clustering method for partitioning object instances into fragments. Our new method applies to existing databases, where statistics are already present. We model fragmentation input data in a vector space and give different object similarity measures together with their geometrical interpretations. We provide quality and performance evaluations using a partition evaluator function