Building XML Data Warehouse Based on Frequent Patterns in User Queries

Abstract

Abstract.With the proliferation of XML-based data sources available across the Internet, it is increasingly important to provide users with a data warehouse of XML data sources to facilitate decision-making processes. Due to the extremely large amount of XML data available on web, unguided warehousing of XML data turns out to be highly costly and usually cannot well accommodate the users' needs in XML data acquirement. In this paper, we propose an approach to materialize XML data warehouses based on frequent query patterns discovered from historical queries issued by users. The schemas of integrated XML documents in the warehouse are built using these frequent query patterns represented as Frequent Query Pattern Trees (FreqQPTs). Using hierarchical clustering technique, the integration approach in the data warehouse is flexible with respect to obtaining and maintaining XML documents. Experiments show that the overall processing of the same queries issued against the global schema become much efficient by using the XML data warehouse built than by directly searching the multiple data sources.

Efficient mining of frequent XML query patterns with repeating-siblings

“…In our mining scheme, we haven't considered the temporal issues which is important in capturing the user querying behavior; another limitation is that we ignore the predicates in the query which are important in a real XML application [47]. These issues will be our future efforts.…”

Finding an application-appropriate model for XML data warehouses

“…For the problem of space and time consumption when assembling data warehouses, Zhang et al [100] build a warehouse from distributed XML data. Data are selected according to their access frequency by users within different distributed sources; hence infrequently accessed data are not loaded within the warehouse.…”

A Survey of XML Tree Patterns

“…TP mining actually summarizes into discovering frequent subtrees in a collection of TPs. It is used, for instance, to cache the results of frequent patterns, which significantly improves query response time [77], produce data warehouse schemas of integrated XML documents from historical user queries [80], or help in website management by mining data streams [84].…”

“…MineFreq further builds upon this principle by mining frequent RST sets [80]. A frequent RST set must satisfy two requirements: 1) support requirement: sup(r 1 , r 2 , ..., r n ) ≥ minsup; 2) confidence requirement: ∀r i , f req(r1,r2,...,rn) f req(ri) ≥ minconf , where minconf is a minimum confidence user-specified threshold.…”