Exploring Application Level Semantics for Data Compression

Natural phenomena shows that many creatures form large social groups and move in regular patterns. These works are mainly focused on finding the movement patterns of each single object or all objects. Here, we tend to initial propose AN economical distributed mining formula to collectively establish a bunch of moving objects and find out their movement patterns in wireless detector networks. Then we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression formula includes a sequence merge and an entropy reduction phases. In the sequence merge section, we propose a Merge algorithm to merge and compress the location data of a group of moving objects. And in the entropy reduction phase, formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we tend to devise 3 replacement rules and derive the utmost compression quantitative relation. The experimental results show that the projected compression formula leverages the cluster movement patterns to cut back the number of delivered knowledge effectively and expeditiously.