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 improve ad-hoc query evaluation performance, Datta @cite_56 propose exploiting a vertical fragmentation of facts to build the index , while Golfarelli @cite_33 propose applying the same fragmentation methodology on data warehouse views. Munneke @cite_61 instead propose an original fragmentation methodology targeted to multidimensional databases. In this case, fragmentation consists in deriving a global data cube from fragments containing a sub-set of data defined by meaningful slice and dice OLAP-like operations @cite_28 @cite_52 . @cite_61 , authors also define an alternative fragmentation strategy, named , which removes one or several dimensions from the target data cube in order to produce fragments having fewer dimensions than the original data cube.
@cite_61 @cite_33 @cite_28 @cite_52 @cite_56
1863762422 2020396904 1986393412 1571314868
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. 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. 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. Fragmentation techniques for XML data are gaining momentum within both distributed and centralized XML query engines and pose novel and unrecognized challenges to the community. Albeit not novel, and clearly inspired by the classical divide et impera principle, fragmentation for XML trees has been proved successful in boosting the querying performance, and in cutting down the memory requirements. However, fragmentation considered so far has been driven by semantics, i.e. built around query predicates. In this paper, we propose a novel fragmentation technique that founds on structural constraints of XML documents (size, tree-width, and tree-depth) and on special-purpose structure histograms able to meaningfully summarize XML documents. This allows us to predict bounding intervals of structural properties of output (XML) fragments for efficient query processing of distributed XML data. An experimental evaluation of our study confirms the effectiveness of our fragmentation methodology on some representative XML data sets.