Positive and negative association rule mining on XML data stream in database as a service concept

Abstract

Due to the development of database technology and systems in recent years, there is an enormous increase in data size. This increase makes the data mining one of the hot topic for organizations to determine their strategies. Association rule mining is a data mining approach that discovers the useful, but hidden patterns in the data set. This method uses widely in traditional databases and usually to find the positive association rules. However, there are some other challenging rule mining topics like data stream mining and negative association rule mining. Nowadays, organizations want to concentrate on their own business and outsource the rest of their work. This approach reveals the "Database as a service" concept. This concept provides lots of benefits to data owner, but, at the same time brings out some security problems. In this research, we have proposed a mining model that combines the mentioned challenging areas. To the best of our knowledge, our approach is unique in the literature. Our model provides efficient solution to find positive and negative association rules on XML data stream in database as a service concept. We have adapted some pruning strategies for efficient negative rule mining. Also, we have applied some security techniques to provide efficient and sufficient data protection. We have run many experiments with some different synthetic data sets and with one real world data set to show the efficiency of our proposed model. The results have shown that proposed system makes the association rules mining operation efficiently.