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.
The problem of selecting an optimal fragmentation schema of a data warehouse is more challenging compared to that in relational and object databases. This challenge is due to the several choices of partitioning star or snowflake schemas. Data partitioning is beneficial if and only if the fact table is fragmented based on the partitioning schemas of dimension tables. This may increase the number of fragments of the fact tables dramatically and makes their maintenance very costly. Therefore, the right selection of fragmenting schemas is important for better performance of OLAP queries. In this paper, we present a genetic algorithm for schema partitioning selection problem. The proposed algorithm gives better solutions since the search space is constrained by the schema partitioning. We conduct several experimental studies using the APB-1 release II benchmark for validating the proposed algorithm. Data warehousing is a booming industry with many interesting research problems. The database research community has concentrated on only a few aspects. In this paper, We summarize the state of the art, suggest architectural extensions and identify research problems in the areas of warehouse modeling and design, data cleansing and loading, data refreshing and purging, metadata management, extensions to relational operators, alternative implementations of traditional relational operators, special index structures and query optimization with aggregates.