Adaptive Distributed Top-k Query Processing

TL;DR

ADiT is an adaptive distributed top-k query processing method that optimizes load and response time in peer-to-peer networks by considering network size, peer capabilities, and data distribution.

Contribution

It introduces an adaptive approach that dynamically optimizes distributed top-k query processing considering multiple network and peer parameters.

Findings

ADiT outperforms existing distributed query techniques in various scenarios.

The approach effectively balances system load and response time.

Experimental results validate the efficiency of ADiT.

Abstract

ADiT is an adaptive approach for processing distributed top-$k$ queries over peer-to-peer networks optimizing both system load and query response time. This approach considers the size of the peer to peer network, the amount $k$ of searched objects, the network capabilities of a connected peer, i.e. the transmission rate, the amount of objects stored on each peer, and the speed of a peer in processing a local top-$k$ query. In extensive experiments with a variety of scenarios we could show that ADiT outperforms state of the art distributed query processing techniques.