Abstract We investigate temporal-clique subgraph pattern matching, where edges must both form a specific topological sub-structure and temporally overlap within a specified window. This problem has widespread applications across domains including social networks, life sciences, smart cities, and telecommunications. However, existing subgraph matching techniques are inefficient at processing such queries that combine both temporal and structural constraints. We propose a novel approach that effectively leverages both topological and temporal selectivities of the query to significantly improve processing performance. Our solution introduces key innovations across the query processing pipeline, including a specialized multi-way join operator, an optimized query planner, and an accurate cardinality estimator. Through additional optimizations, we further enhance the efficiency of our approach. Extensive experiments demonstrate that our method substantially outperforms state-of-the-art techniques while requiring minimal additional storage overhead.

Property Graphs are a versatile and expressive data model that has gained widespread adoption due to their flexibility in supporting labeled and attributed nodes and edges. They are well-established in research communities and are becoming widespread in companies and organizations across various sectors. They have been boosted by a fervent ISO/IEC standardization activity, leading to dedicated query and schema languages. While the current standards are still evolving, opportunities remain to enrich them with features such as composability. The plethora of existing query languages reflects a rich and diverse ecosystem, which ongoing unification efforts aim to align. This tutorial aims to deepen the understanding of Property Graph standards by showcasing their strengths, highlighting recent unification efforts, clarifying the central role of schema constraints, and exploring the rich landscape of research and industrial opportunities shaping the future of graph data management.

Abstract We study the optimization of navigational graph queries in the form of the Regular Queries, i.e., queries which combine recursive and pattern-matching fragments. Current approaches to their evaluation are not effective in practice. Towards addressing this, we present a number of novel powerful optimization techniques which aim to constrain the intermediate results during query evaluation. We show how these techniques can be planned effectively and executed efficiently towards the first practical evaluation solution for complex navigational queries on real-world workloads. Indeed, our experimental results show several orders of magnitude improvement in query evaluation performance over state-of-the-art techniques on a wide range of queries on diverse datasets.

Knowledge graphs have soared in popularity by supporting different types of applications and domains. In this context, the property graph data model has become an emerging standard in industry and academia. With its widespread use, there is also an increasing interest in investigating constraints for property graph data and their applications in data profiling. Graph Generating Dependencies (GGDs) are a class of property graph data dependencies that can express constraints on topology and properties of nodes and edges of the graph, making them a suitable candidate to expose an overview of the property graph to the user (profile graph data). However, GGDs can be difficult to set manually. To solve this issue, we propose a framework for discovering GGDs automatically from the property graph to profile graph data. Our framework has three main steps: (1) pre-processing, (2) candidate generation, and, (3) GGD extraction. Our results show that the discovered set of GGDs can give an overview of the input graph, including schema-level information between the graph patterns and attributes.

Data dependencies are a key concept in data management and have been researched in data integration, data quality and query optimization. With the increasing use of graph-structured data in diverse applications, there is also an increasing interest in the study of graph data dependencies. In this scenario different classes of graph data dependencies have been proposed in the literature. In this work we study the class of Graph Generating Dependencies (GGDs). Graph Generating Dependencies (GGDs) informally express constraints between two (possibly different) graph patterns which enforce relationships on both graph's data (via property value constraints) and its structure (via topological constraints). While most of previously proposed classes of graph data dependencies focus on generalizing equality-generating dependencies for graph data, Graph Generating Dependencies (GGDs) can express tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. Given this new class of dependency, in this paper, we discuss the reasoning behind GGDs on Property Graphs. We propose algorithms to solve three main reasoning problems: the satisfiability, implication, and validation problems for GGDs and analyze their complexity. By studying these problems, we can understand the expressiveness and the limitations of GGDs in practical applications. To demonstrate the practical use of GGDs, we propose an algorithm that finds inconsistencies in data through validation of GGDs. Our experiments show that even though the validation of GGDs has high computational complexity, GGDs can be used to find data inconsistencies in a feasible execution time on both synthetic and real-world data.

We introduce a framework for cardinality estimation of query patterns over property graph databases. This framework makes it possible to analyze, compare and combine different cardinality estimation approaches. It consists of three phases: obtaining a set of estimates for some subqueries, extending this set and finally combining the set into a single cardinality estimate for the query. We show that (parts of) many existing cardinality estimation approaches can be used as techniques in one of the phases from our framework. The phases are loosely coupled, making it possible to combine (parts of) current cardinality estimation approaches. We created a graph version of the Join Order Benchmark to perform experiments with different combinations of techniques. The results showed that query patterns without property constraints can be accurately estimated using synopses for small patterns. Accurate estimation of query patterns with property constraints require new estimation techniques to be developed that capture correlations between the property constraints and the topology in graph databases.

We demonstrate AvantGraph, a graph query processing engine developed by the Database group at TU Eindhoven. Designed for efficient processing of both subgraph matching and navigational graph queries, AvantGraph encompasses innovation in three key areas: the planner, the cardinality estimator, and the execution engine. We present demonstration scenarios covering a wide range of workloads across diverse domains which (1) provides deep insights into the core challenges of complex graph query processing and (2) showcases corresponding critical optimizations via "under-the-hood" operational insights of AvantGraph's key components.

We study the problem of temporal-clique subgraph pattern matching. In such patterns, edges are required to jointly overlap in time within a given temporal window in addition to forming a topological sub-structure. This problem arises in many application domains, e.g., in social networks, life sciences, smart cities, telecommunications, and others. State-of-the-art subgraph matching techniques, however, are shown to be limited and inefficient in processing queries with both temporal and topological constraints. We propose an approach that takes full advantage of both topological and temporal selectivities during the processing of temporal-clique subgraph queries. Additionally, we investigate a number of optimizations that can be introduced into our approach to improve its efficiency. Our experimental results demonstrate that our approach outperforms the existing methods by a wide margin at a small additional storage cost.

We propose Graph Generating Dependencies (GGDs), a new class of dependencies for property graphs. Extending the expressivity of state of the art constraint languages, GGDs can express both tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. We provide the formal definition of GGDs, analyze the validation problem for GGDs, and demonstrate the practical utility of GGDs.

We study the problem of enumeration of all k-sized subsets of temporal events that mutually overlap at some point in a query time window. This problem arises in many application domains, e.g., in social networks, life sciences, smart cities, telecommunications, and others. We propose a start time index (STI) approach that overcomes the efficiency bottlenecks of current methods which are based on 2-way join algorithms to enumerate temporal k-cliques. Additionally, we investigate how precomputed checkpoints can be used to further improve the efficiency of STI. Our experimental results demonstrate that STI outperforms the state of the art by a wide margin and that our checkpointing strategies are effective.

Context-free path queries extend regular path queries for increased expressiveness. A context-free grammar is used to recognize accepted paths by their label strings, or traces. Such queries arise naturally in graph analytics, e.g., in bioinformatics applications. Currently, the practical performance of methods for context-free path query evaluation is not well understood. In this work, we study three state of the art context-free path query evaluation methods. We measure the performance of these methods on diverse query workloads on various data sets and compare their results. We showcase how these evaluation methods scale as graphs get bigger and queries become larger or more ambiguous. We conclude that state of the art solutions are not able to cope with large graphs as found in practice.

We aim to improve the accuracy of path selectivity estimation in graph databases by intelligently ordering the domain of a histogram used for estimation. This problem has not, to our knowledge, received adequate attention in the research community. We present a novel framework for the systematic study of path ordering strategies in histogram construction and use. In this framework, we introduce new ordering strategies which we experimentally demonstrate lead to significant improvement of the accuracy of path selectivity estimation over current strategies. These positive results highlight the fundamental role that domain ordering plays in the design of effective histograms for efficient and scalable graph query processing.

Regular path queries (RPQs) have quickly become a staple to explore graph databases. SPARQL 1.1 includes prop- erty paths, and so now encompasses RPQs as a fragment. Despite the extreme utility of RPQs, it can be exceedingly difficult for even experts to formulate such queries. It is next to impossible for non-experts to formulate such path queries. As such, several visual query systems (VQSs) have been proposed which simplify the task of constructing path queries by directly manipulating visual objects representing the domain elements. The queries generated by VQSs may, however, have many commonalities that can be exploited to optimize globally. We introduce Tasweet, a framework for optimizing “disjunctive” path queries, which detects the commonalities among the queries to find a globally opti- mized execution plan over the plan spaces of the constituent RPQs. Our results show savings in edge-walks / time-to- completion of 59%.

With the recent resurgence of interest in graph data man- agement, there has been a flurry of research on the design and engineering of graph query languages. On the design side, there is a large body of theoretical results that have been obtained regarding graph languages. On the engineer- ing side, many sophisticated scalable solutions for graph query processing have been developed and put into practice. While both areas are focusing on the study of graph query languages, there has been relatively little work bridging the results on both sides. This tutorial will survey the state of the art in this landscape with a particular focus on uncovering and highlighting indicative research issues that are ripe for collaboration and cross-fertilization between the engineering and theoretical studies of graph database systems.

International audience

The extension of SPARQL in version 1.1 with property paths offers a type of regular path query for RDF graph databases. Such queries are difficult to optimize and evaluate efficiently, however. We have embarked on a project, Waveguide, to build a cost-based optimizer for SPARQL queries with property paths. Waveguide builds a query plan--- which we call a waveplan (WP)--- which guides the query evaluation. There are numerous choices in the construction of a plan, and a number of optimization methods, so the space of plans for a query can be quite large. Execution costs of plans for the same query can vary by orders of magnitude. A WGP's costs can be estimated, which opens the way to cost-based optimization. We demonstrate that the plan space of Waveguide properly subsumes existing techniques and that the new plans it adds are relevant.

The extension of SPARQL 1.1 of property paths now o↵ers a type of regular path query for RDF graph databases. While eminently useful, these queries are dicult to optimize to evaluate eciently. We have embarked on a project we call Waveguide to build a cost-based optimizer for SPARQL queries with property paths. Waveguide maps the property path to a waveguide plan (WGP )c omposed ofwavefront automata (WFAs) modeled by (non-deterministic) finite automata. The waveguide plan guides the graph search during evaluation. Our Waveguide prototype illustrates the types of optimizations this approach a↵ords and the performance gains that can be obtained.