A False Negative Oriented Approach for Efficient Data Stream Analysis in Ubiquitous Environment

In recent years, advances in hardware technology have facilitated the ability to collect data continuously. Simple transactions of everyday life such as using a credit card, a phone or browsing the web lead to automated data storage. In this paper, we are focuses on finding frequent itemsets over ubiquitous data streams. The proposed method shows that false negative oriented approach that allows a controlled number of frequent itemsets missing from the output is a more promising solution for mining frequent itemsets. Therefore, we show that our method is simple to implement, and have provable quality, space, and time guarantees.