Tracking Federated Queries in the Linked Data

TL;DR

FETA is a novel algorithm that enables data providers in Linked Data federations to infer and track the original federated queries from shared logs, despite the complexity of distributed subqueries.

Contribution

This paper introduces FETA, the first method for inferring federated queries from subquery logs in Linked Data environments, addressing a key privacy and transparency challenge.

Findings

FETA successfully extracts BGPs containing original query patterns.

FETA performs well even under worst-case concurrent subquery execution.

Experiments with Anapsid demonstrate effective query tracking.

Abstract

Federated query engines allow data consumers to execute queries over the federation of Linked Data (LD). However, as federated queries are decomposed into potentially thousands of subqueries distributed among SPARQL endpoints, data providers do not know federated queries, they only know subqueries they process. Consequently, unlike warehousing approaches, LD data providers have no access to secondary data. In this paper, we propose FETA (FEderated query TrAcking), a query tracking algorithm that infers Basic Graph Patterns (BGPs) processed by a federation from a shared log maintained by data providers. Concurrent execution of thousand subqueries generated by multiple federated query engines makes the query tracking process challenging and uncertain. Experiments with Anapsid show that FETA is able to extract BGPs which, even in a worst case scenario, contain BGPs of original queries.