ADQUEX: Adaptive Processing of Federated Queries over Linked Data based on Tuple Routing

TL;DR

ADQUEX is an adaptive federated query processing method over linked data that dynamically adjusts execution plans at runtime without relying on prior statistical data, improving efficiency especially for complex queries.

Contribution

It introduces ADQUEX, a novel adaptive approach for federated query processing that operates effectively without prior statistical information and adapts to runtime conditions.

Findings

Reduces intermediate results during query execution.

Adapts to network latencies and environment changes.

Performs well on complex queries over linked datasets.

Abstract

Due to the distribution of linked data across the web, the methods that process federated queries through a distributed approach are more attractive to the users and have gained more prosperity. In distributed processing of federated queries, we need methods and procedures to execute the query in an optimal manner. Most of the existing methods perform the optimization task based on some statistical information, whereas the query processor does not have precise statistical information about their properties, since the data sources are autonomous. When precise statistics are not available, the possibility of wrong estimations will highly increase, and may lead to inefficient execution of the query at runtime. Another problem of the existing methods is that in the optimization phase, they assume that runtime conditions of query execution are stable, while the environment in which federated queries are executed over linked data is dynamic and non-predictable. By considering these two problems, there is a great potential for exploiting the federated query processing techniques in an adaptive manner. In this paper, an adaptive method is proposed for processing federated queries over linked data, based on the concept of routing the tuples. The proposed method, named ADQUEX, is able to execute the query effectively without any prior statistical information. This method can change the query execution plan at runtime so that less intermediate results are produced. It can also adapt the execution plan to new situation if unpredicted network latencies arise. Extensive evaluation of our method by running real queries over well-known linked datasets shows very good results especially for complex queries.