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
1863762422 2020396904 2039795745 2142490918
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. This paper addresses the vertical partitioning of a set of logical records or a relation into fragments. The rationale behind vertical partitioning is to produce fragments, groups of attribute columns, that “closely match” the requirements of transactions. Vertical partitioning is applied in three contexts: a database stored on devices of a single type, a database stored in different memory levels, and a distributed database. In a two-level memory hierarchy, most transactions should be processed using the fragments in primary memory. In distributed databases, fragment allocation should maximize the amount of local transaction processing. Fragments may be nonoverlapping or overlapping. A two-phase approach for the determination of fragments is proposed; in the first phase, the design is driven by empirical objective functions which do not require specific cost information. The second phase performs cost optimization by incorporating the knowledge of a specific application environment. The algorithms presented in this paper have been implemented, and examples of their actual use are shown. 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.