An Agent-Based Adaptive Join Algorithm for Distributed Data Warehousing

Data warehousing continues to play an important role in global information systems for businesses. Meanwhile, applications of data warehousing have evolved from reporting and decision support systems to mission critical decision making systems. This requires data warehouses to combine both historical and current data from operational systems. Since a join operation is one of the most expensive operations in query processing, it is vital to develop effective and efficient join techniques for a distributed warehouse environment. In this paper, we propose an agent-based adaptive join algorithm called AJoin for effective and efficient online join operations in distributed data warehouses. AJoin utilises intelligent agents for dynamic optimisation and coordination of join processing at run time. Key aspects of the AJoin algorithm have been implemented and evaluated against other modern adaptive join algorithms. It has been shown that AJoin exhibits better performance under various distributed and dynamic data warehouse environments in our study.