Integrating Radio Frequency Object Discovery and Bluetooth for Semantic-Based M-Commerce
RFID and current ubiquitous protocols and technologies call for an integration, to provide innovative and advanced wireless services, with semantics being a candidate glue for such an integration. To this aim, in this paper we study an enhancement to the original RFID data exchange protocol and its integration in a semantic-based Bluetooth resource discovery framework. The semantic annotation of products and goods can then be exploited to allow an effective retrieval process. The proposed approach is presented and motivated in a novel m-commerce context.
Many efficient association rule mining algorithms have been proposed in the literature. In this paper, we propose an algorithm FRM (Mining Frequent Itemsets by Frequent-Related Mechanism). Most of the studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate generation is still costly when there exist a large number of long patterns. FRM scans database only four times and it does not adopt the Apriori-like approach in mining process. It uses the frequent-related mechanism to generate the itemsets which are the most possible to be frequent and it eliminates a great number of infrequent itemsets. So FRM is very suitable to mine the databases whose record length is very long.