Multi-keyword search over P2P based on Bloom filter

Abstract

In keyword search over Peer-to-Peer ( P2P) based on standard Bloom Filter ( BF) , it is difficult to estimate the maximum number of the data sets because they are increasing continuously; hence, two problems show up: it is difficult to determine the upper value of the length of BF vector, and it cannot handle the multi-keyword search efficiently. To solve these problems, the authors proposed a new structure called Block Dynamic Bloom Filter ( BDBF) which partitioned the keyword based on the frequency, and presented a new Top-k multi-keyword search model: the node sends the higher frequency DBF firstly, and then sends the secondary higher frequency DBF if need. The experimental results show that the proposed method can be applicable for the increasing data of index list, and it can also decrease the network's traffic and efficiently resolve the problem of Top-k query in multi-keyword search over P2P.