5 research outputs found
Data Mining-based Fragmentation of XML Data Warehouses
With the multiplication of XML data sources, many XML data warehouse models
have been proposed to handle data heterogeneity and complexity in a way
relational data warehouses fail to achieve. However, XML-native database
systems currently suffer from limited performances, both in terms of manageable
data volume and response time. Fragmentation helps address both these issues.
Derived horizontal fragmentation is typically used in relational data
warehouses and can definitely be adapted to the XML context. However, the
number of fragments produced by classical algorithms is difficult to control.
In this paper, we propose the use of a k-means-based fragmentation approach
that allows to master the number of fragments through its parameter. We
experimentally compare its efficiency to classical derived horizontal
fragmentation algorithms adapted to XML data warehouses and show its
superiority
Dynamic index selection in data warehouses
International audienceAnalytical queries defined on data warehouses are complex and use several join operations that are very costly, especially when run on very large data volumes. To improve response times, data warehouse administrators casually use indexing techniques. This task is nevertheless complex and fastidious. In this paper, we present an automatic, dynamic index selection method for data warehouses that is based on incremental frequent itemset mining from a given query workload. The main advantage of this approach is that it helps update the set of selected indexes when workload evolves instead of recreating it from scratch. Preliminary experimental results illustrate the efficiency of this approach, both in terms of performance enhancement and overhead
Dynamic index selection in data warehouses
International audienceAnalytical queries defined on data warehouses are complex and use several join operations that are very costly, especially when run on very large data volumes. To improve response times, data warehouse administrators casually use indexing techniques. This task is nevertheless complex and fastidious. In this paper, we present an automatic, dynamic index selection method for data warehouses that is based on incremental frequent itemset mining from a given query workload. The main advantage of this approach is that it helps update the set of selected indexes when workload evolves instead of recreating it from scratch. Preliminary experimental results illustrate the efficiency of this approach, both in terms of performance enhancement and overhead