Regular path queries (RPQs) are at the heart of navigational queries in graph databases. Motivated by new features of regular path queries in the languages Cypher, GQL, and SQL/PGQ, which require new approaches for indexing and compactly storing intermediate query results, we investigate a large corpus of real-world RPQs. Our corpus consists of 148.7 million RPQs occurring in 937.2 million SPARQL queries, used on 29 different data sets. We investigate three main questions on these logs. First, what is the syntactic structure of these RPQs? Second, how much nondeterminism (or ambiguity) do they have? Third, do they admit tractable evaluation under simple path and trail semantics? Concerning the first question, we show that all the RPQs can be classified in only 572 different syntactic shapes, which we provide in a downloadable data set in Zenodo. Furthermore, we classify the relative use of various RPQ operators, and popular predicates that are used for transitive navigation. Concerning the second question, we show that although non-determinism occurs in the RPQs, less than one in ten million requires a deterministic finite automaton with more states than the size of the regular expression. This is remarkable because this blow-up is known to be exponential in the worst case. Concerning the last question, the vast majority of expressions admits tractable evaluation.
Graph Neural Networks (GNNs) have the potential to address realworld problems involving continuously evolving, graph-structured data, such as fraud detection, real-time recommendations, and traffic monitoring. These applications require the timely processing of streaming (possibly unbounded) data, highlighting the need of integrating GNN inference with dataflow stream processing systems, like Apache Flink. In this paper, we present the first exploration of bridging this gap, by designing a streaming GNN serving pipeline with Flink and PyTorch. We propose a dataflow architecture that offloads subgraph construction to Flink, leveraging its state management and distributed processing capabilities. Despite achieving viable performance through asynchronous inference requests and careful parallelism tuning, we also identify significant limitations stemming from Flink's state scoping, lack of iterative processing, and computation pipelining. We propose solutions that mitigate these issues within Flink and discuss open challenges towards developing dataflow systems tailored to streaming GNN inference.
Property graph databases are widely used in applications such as social networks and fraud detection due to their ability to model relationships between entities efficiently. However, outsourcing graph data to third-party cloud providers introduces significant privacy risks, as memory access patterns and result sizes can leak sensitive information even when data is encrypted. While prior work has developed oblivious relational databases to mitigate such attacks, no existing solution supports oblivious query processing for property graphs, which pose unique challenges beyond those addressed in relational settings. We present a vision for an oblivious property graph database that supports Cypher queries on encrypted outsourced data. We envision leveraging secure hardware enclaves such as Intel SGX/TDX and AMD SEV-SNP to protect query execution while novel query processing algorithms mitigate side-channel vulnerabilities through doubly oblivious query processing schemes. Specifically, we discuss the challenges in supporting oblivious query processing algorithms for multi-hop and cyclic queries without revealing data or memory access patterns. Our work highlights a crucial gap and potential research directions in oblivious query processing for property graph databases.
