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Association rule mining is one of the most important and basic technique in data mining,which has been studied extensively and has a wide range of applications.However,as traditional data mining algorithms usually only focus on analyzing data organized in single table,applying these algorithms in multi-relational data environment will result in many problems.This paper summarizes these problems,proposes a framework for the mining of multi-relational association rule,and gives a definition of the mining task.After classifying the existing work into two categories,it describes the main techniques used in several typical algorithms,and it also makes comparison and analysis among them.Finally,it points out some issues unsolved and some future further research work in this area.

In this paper, a strategy for view maintenance in peer data management systems (PDMSs) is proposed. First, a hybrid peer architecture is presented, in which peers prefer peer-to-peer architecture to super-peer based architecture. Then, if a view is reformulated along a semantic path, it can retrieve data from many data sources. So the peer view, local view and global view are proposed. A global view maintenance is turned into the maintenance of all related local views if join operations are confined in each local PDMS, and a query can gain the same result if it is posed over any peer in the same semantic path. Furthermore, a view may be a conjunctive query which relates to several tables in PDMSs. According to the application, Mork’s rules are extended. Based on the new rule system, the update data are propagated in PDMS. According to the new rule system, a view maintenance algorithm in PDMS is proposed. Finally, simulation experiments are carried out in the SPDMS (schema-mapping-based PDMS). The simulation results show that the proposed view maintenance strategy has better performance than that of Mork’s.

Recently, ontology learning is emerging as a new hotspot of research in computer science. In this paper the issue of ontology learning is divided into nine sub-issues according to the structured degree (structured, semi-structured, non-structured) of source data and learning objects (concept, relation, axiom) of ontology. The characteristics, major approaches and the latest research progress of the nine sub-issues are summarized. Based on the analysis framework proposed in the paper, existing ontology learning tools are introduced and compared. The problems of current research are discussed, and finally the future directions are pointed out.