# The Odyssey Approach for Optimizing Federated SPARQL Queries

arXiv: https://arxiv.org/abs/1705.06135 · HTML: https://mlsanity.com/paper/1705.06135/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

## Built on by (most cited first, 5 of 5)

- [Agentic SPARQL: Evaluating SPARQL-MCP-powered Intelligent Agents on the Federated KGQA Benchmark](https://mlsanity.com/paper/2603.06582/) (2026)
- [KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science](https://mlsanity.com/paper/2303.02204/) (2023)
- [An Empirical Evaluation of Cost-based Federated SPARQL Query Processing Engines](https://mlsanity.com/paper/2104.00984/) (2021)
- [A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions](https://mlsanity.com/paper/2402.12001/) (2024)
- [Semantic Web: Past, Present, and Future (with Machine Learning on Knowledge Graphs and Language Models on Knowledge Graphs)](https://mlsanity.com/paper/2412.17159/) (2024)

## Abstract (arXiv)

Answering queries over a federation of SPARQL endpoints requires combining data from more than one data source. Optimizing queries in such scenarios is particularly challenging not only because of (i) the large variety of possible query execution plans that correctly answer the query but also because (ii) there is only limited access to statistics about schema and instance data of remote sources. To overcome these challenges, most federated query engines rely on heuristics to reduce the space of possible query execution plans or on dynamic programming strategies to produce optimal plans. Nevertheless, these plans may still exhibit a high number of intermediate results or high execution times because of heuristics and inaccurate cost estimations. In this paper, we present Odyssey, an approach that uses statistics that allow for a more accurate cost estimation for federated queries and therefore enables Odyssey to produce better query execution plans. Our experimental results show that Odyssey produces query execution plans that are better in terms of data transfer and execution time than state-of-the-art optimizers. Our experiments using the FedBench benchmark show execution time gains of at least 25 times on average.