A new ant colony optimization-based algorithm for range query answering problem in relational schema-based P2P database systems

Nowadays, peer-to-peer database systems (P2PDBSs) aiming at data sharing in the Web have become very popular. Due to the absence of global knowledge about data placement in unstructured P2P networks, query processing and answering is a challenging problem in such systems. This process is provided by query routing and is an optimization problem whose goal is to find the maximum results with spending a predetermined cost. With the aim of improving the efficiency of range query answering algorithm in relational schema-based P2PDBSs, the present study, for the first time, adapts the ant colony metaphor with range query answering problem in relational schema-based P2P systems and proposes a new algorithm using the ant colony optimization approach in which the researchers use histogram data structure and apply both positive and negative feedbacks, dynamic learning rate, and local heuristic mechanisms and show that the proposed algorithm gives better results than the comparative greedy-based method. The experimental tests indicate that in the best case, the average number of traveled links for finding one answer (i.e., cost-to-answers ratio) is decreased almost by half in contrast to that of greedy-based algorithm. Furthermore, the achieved results indicate that the proposed algorithm is completely flexible with the users’ requests, i.e., more query answers or less query response time, and the algorithm parameters can be properly set to meet the users’ requests.