An Analytical Study of Large SPARQL Query Logs (1708.00363v1)

Abstract: With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end- users has become more and more common in SPARQL end- points. In this paper, we conduct an in-depth analytical study of the queries formulated by end-users and harvested from large and up-to-date query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, span- ning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries, that exhibits already interesting results on this generalized corpus, we drill deeper in the structural char- acteristics related to the graph- and hypergraph represen- tation of queries. We outline the most common shapes of queries when visually displayed as pseudographs, and char- acterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel con- cept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users, and brings us to draw a number of conclusions and pinpoint future di- rections for SPARQL query evaluation, query optimization, tuning, and benchmarking.

An Analytical Study of Large SPARQL Query Logs

This paper presents a comprehensive analysis of a vast corpus of SPARQL query logs derived from various RDF data sources spanning multiple years. The primary aim is to understand the characteristics of queries made by end-users on SPARQL endpoints, focusing on the syntactic and structural aspects of these queries.

Key Contributions

1. Corpus Composition and Query Characteristics: The paper examines query logs from diverse datasets, including DBpedia, BioPortal, LinkedGeoData, and others, totaling more than 180 million queries. The analysis reveals a predominance of Select and Ask queries, with Describe and Construct queries being less common. The prevalence of simple queries (those with few triple patterns) is noted across the dataset, though complex queries with more extensive triple patterns are also present, particularly in DBpedia logs.
2. Syntactic and Operator Usage: The paper provides a detailed breakdown of SPARQL features used in the queries. Select queries dominate, while the use of various SPARQL constructs such as Filter, Union, and Opt indicates diverse query formulations. It is noteworthy that the use of projection significantly impacts the complexity of query evaluation in SPARQL.
3. Structural Analysis: A deep dive into the graph and hypergraph structures of queries is conducted, especially focusing on the canonical graph representations of AOF (And/Opt/Filter) patterns. The paper identifies that most queries are tree-like, with many being classified as chains, cycles, or flowers based on their graph structure. This classification has implications for query evaluation efficiency, given the correlation between tree-like structures and polynomial-time query evaluation.
4. Performance Evaluation: The paper includes synthetic performance tests on different graph query engines, highlighting the discrepancy in handling cyclic and acyclic queries. The results suggest a performance gap that could drive improvements in query optimization techniques.
5. Temporal Query Evolution: Introducing the concept of streaks, the paper analyzes the evolution of queries over time. This offers insights into user behavior, showing patterns of query refinement indicative of the exploratory nature of SPARQL querying by end-users.

Implications and Future Directions

The findings underscore the need for improvement in query processing systems, especially in handling complex and cyclic queries efficiently. The structural insights from the large-scale analysis could guide the development of optimized query engines and the design of targeted benchmarks. The observed prevalence of certain query structures might also inform the design of future query languages and optimization techniques.

Further research could focus on enhancing the capability of SPARQL engines to process complex query structures more efficiently, potentially incorporating advanced decomposition and caching strategies. Additionally, evolving user queries (streaks) suggest an opportunity for systems to provide better support for query refinement processes, potentially through interactive tools or real-time optimization feedback.

Overall, this paper provides a foundational analysis of SPARQL query usage, highlighting the diversity and complexity of user queries and setting the stage for future advancements in RDF data processing technologies.