We showcase SHARQL, a system that allows to navigate SPARQL query logs, can inspect complex queries by visualizing their shape, and can serve as a back-end to flexibly produce statistics about the logs. Even though SPARQL query logs are increasingly available and have become public recently, their navigation and analysis is hampered by the lack of appropriate tools. SPARQL queries are sometimes hard to understand and their inherent properties, such as their shape, their hypertree properties, and their property paths are even more difficult to be identified and properly rendered. In SHARQL, we show how the analysis and exploration of several hundred million queries is possible. We offer edge rendering which works with complex hyperedges, regular edges, and property paths of SPARQL queries. The underlying database stores more than one hundred attributes per query and is therefore extremely flexible for exploring the query logs and as a back-end to compute and display analytical properties of the entire logs or parts thereof.

This paper provides an in-depth and diversified analysis of the Wikidata query logs, recently made publicly available. Although the usage of Wikidata queries has been the object of recent studies, our analysis of the query traffic reveals interesting and unforeseen findings concerning the usage, types of recursion, and the shape classification of complex recursive queries. Wikidata specific features combined with recursion let us identify a significant subset of the entire corpus that can be used by the community for further assessment. We considered and analyzed the queries across many different dimensions, such as the robotic and organic queries, the presence/absence of constants along with the correctly executed and timed out queries. A further investigation that we pursue in this paper is to find, given a query, a number of queries structurally similar to the given query. We provide a thorough characterization of the queries in terms of their expressive power, their topological structure and shape, along with a deeper understanding of the usage of recursion in these logs. We make the code for the analysis available as open source.

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 endpoints. 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, spanning 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 characteristics related to the graph and hypergraph representation of queries. We outline the most common shapes of queries when visually displayed as undirected graphs, and characterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel concept 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 directions for SPARQL query evaluation, query optimization, tuning, and benchmarking.

Regular expressions are omnipresent in database applications. They form the structural core of schema languages for XML, they are a fundamental ingredient for navigational queries in graph databases, and are being considered in languages for upcoming technologies such as schema- and transformation languages for tabular data on the Web. In this paper we study the usage and effectiveness of the counting operator (or: limited repetition) in regular expressions. The counting operator is a popular extension which is part of the POSIX standard and therefore also present in regular expressions in grep, Java, Python, Perl, and Ruby. In a database context, expressions with counting appear in XML Schema and languages for querying graphs such as SPARQL 1.1 and Cypher.