Ranking distributed database in tuple-level uncertainty

Ranking in uncertain database environments has gained a great importance recently. Many techniques were introduced to rank uncertain databases and others to rank distributed certain databases. Unfortunately, there are not that much techniques in ranking distributed uncertain databases. This paper proposes a framework that improves ranking processing in the case of uncertain and distributed database. In the proposed framework, new communication and computation-efficient algorithms are investigated for retrieving the top-k tuples from distributed sites. These algorithms are applied in tuple-level uncertainty. The main concern of the proposed algorithms is to reduce the communication rounds utilized and amount of data transmitted while achieving efficient ranking. Experimental results emphasize that both proposed algorithms have a great impact on reducing communication cost. Also, the results clarify that the first algorithm is efficient in the case of a low number of sites while the second achieves better performance in the context of a higher number of sites.