arXiv subjects

Nikolay Yakovets

Publications and source records attributed to Nikolay Yakovets.

14 recordsLinked to original sources

arXiv2026

Foundations of the GraphAlg Language

The GraphAlg domain-specific language for graph algorithms enables user-defined algorithms in graph databases. In this work we show how GraphAlg is built on top of the formal MATLANG language for matrix manipulation. Starting from MATLANG, we describe the extensions to MATLANG and the syntactic sugar needed to derive GraphAlg. Furthermore, we prove that any GraphAlg program can be simulated in an extension of for-MATLANG that supports simultaneous induction.

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arXiv2026

GraphAlg Playground: An Online Platform for Learning and Experimenting with the GraphAlg Language

The GraphAlg language for graph algorithms enables native support for user-defined graph analytics workloads in databases. In this demonstration, we present a web-based playground for writing and executing GraphAlg programs in the web browser, including an interactive tutorial explaining its key concepts. The playground runs inside the user's web browser without any installation, and is freely available under a permissive license as a reusable library. We present two demonstration scenarios of the publicly available playground website, showing how new users can learn to program in GraphAlg using the tutorial, while expert users can use the playground to prototype and validate their algorithms.

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arXiv2026

Optimizing Navigational Graph Queries

We study the optimization of navigational graph 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.

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arXiv2026

Even Faster Geosocial Reachability Queries

Geosocial reachability queries (\textsc{RangeReach}) determine whether a given vertex in a geosocial network can reach any spatial vertex within a query region. The state-of-the-art 3DReach method answers such queries by encoding graph reachability through interval labelling and indexing spatial vertices in a 3D R-tree. We present 2DReach, a simpler approach that avoids interval labelling entirely. Like 3DReach, 2DReach collapses strongly connected components (SCCs) into a DAG, but instead of computing interval labels, it directly stores a 2D R-tree per component over all reachable spatial vertices. A query then reduces to a single 2D R-tree lookup. We further propose compressed variants that reduce storage by excluding spatial sinks and sharing R-trees between components with identical reachable sets. Experiments on four real-world datasets show that 2DReach achieves faster index construction than 3DReach, with the compressed variant yielding the smallest index size among all methods. 2DReach delivers competitive or superior query performance with more stable response times across varying query parameters.

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arXiv2026

Algorithm Support for Graph Databases, Done Right

Graph database query languages cannot express algorithms like PageRank, forcing costly data wrangling, while existing solutions such as algorithm libraries, vertex-centric APIs, and recursive CTEs lack the necessary combination of expressiveness, performance, and usability. We present GraphAlg: a domain-specific language for graph algorithms that compiles to relational algebra, enabling seamless integration with query processing pipelines. Built on linear algebra foundations, GraphAlg provides intuitive matrix operations that are amenable to aggressive optimization including sparsity analysis, loop-invariant code motion, and in-place aggregation. Our implementation in AvantGraph demonstrates significant code complexity reduction compared to SQL/Python and Pregel while achieving excellent performance on LDBC Graphalytics benchmarks. GraphAlg establishes that graph databases can serve as unified platforms for both queries and analytics.

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arXiv2025

Meta-Property Graphs: Extending Property Graphs with Metadata Awareness and Reification

The ISO standard Property Graph model has become increasingly popular for representing complex, interconnected data. However, it lacks native support for querying metadata and reification, which limits its abilities to deal with the demands of modern applications. We introduce the vision of Meta-Property Graphs, a backwards compatible extension of the property graph model addressing these limitations. Our approach enables first-class treatment of labels and properties as queryable objects and supports reification of substructures in a graph. We propose MetaGPML, a backwards compatible extension of the Graph Pattern Matching Language forming the core of the ISO standard GQL, to query these enhanced graphs. We demonstrate how these foundations pave the way for advanced data analytics and governance tasks that are challenging or impossible with current property graph systems.

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arXiv2025

On the feasibility of semantic query metrics

We consider the problem of defining semantic metrics for relational database queries. Informally, a semantic query metric for a query language $L$ is a metric function $δ:L\times L\to \mathbb{N}$ where $δ(Q_1, Q_2)$ represents the length of a shortest path between queries $Q_1$ and $Q_2$ in a graph. In this graph, nodes are queries from $L$, and edges connect semantically distinct queries where one query is maximally semantically contained in the other. Since query containment is undecidable for first-order queries, we focus on the simpler language of conjunctive queries. We establish that defining a semantic query metric is impossible even for conjunctive queries. Given this impossibility result, we identify a significant subclass of conjunctive queries where such a metric is feasible, and we establish the computational complexity of calculating distances within this language.

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arXiv2025

Discovering Graph Generating Dependencies for Property Graph Profiling

With the increasing use of graph-structured data, there is also increasing interest in investigating graph data dependencies and their applications, e.g., in graph data profiling. Graph Generating Dependencies (GGDs) are a class of dependencies for property graphs that can express the relation between different graph patterns and constraints based on their attribute similarities. Rich syntax and semantics of GGDs make them a good candidate for graph data profiling. Nonetheless, GGDs are difficult to define manually, especially when there are no data experts available. In this paper, we propose GGDMiner, a framework for discovering approximate GGDs from graph data automatically, with the intention of profiling graph data through GGDs for the user. GGDMiner has three main steps: (1) pre-processing, (2) candidate generation, and, (3) GGD extraction. To optimize memory consumption and execution time, GGDMiner uses a factorized representation of each discovered graph pattern, called Answer Graph. Our results show that the discovered set of GGDs can give an overview about the input graph, both schema level information and also correlations between the graph patterns and attributes.

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arXiv2022

Reasoning on Property Graphs with Graph Generating Dependencies

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). Graph Generating Dependencies (GGDs) can express tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. In this paper, we discuss the reasoning behind GGDs. We propose algorithms to solve the satisfiability, implication, and validation problems for GGDs and analyze their complexity. To demonstrate the practical use of GGDs, we propose an algorithm which 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.

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arXiv2021

A General Cardinality Estimation Framework for Subgraph Matching in Property Graphs

Many techniques have been developed for the cardinality estimation problem in data management systems. In this document, we introduce a framework for cardinality estimation of query patterns over property graph databases, which makes it possible to analyze, compare and combine different cardinality estimation approaches. This framework 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 of the existing cardinality estimation approaches can be used as techniques in one of the phases from our framework. The three phases are loosely coupled, this makes it possible to combine (parts of) current cardinality estimation approaches. We create a graph version of the Join Order Benchmark to perform experiments with different combinations of techniques. The results show 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.

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arXiv2020

The Future is Big Graphs! A Community View on Graph Processing Systems

Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?

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arXiv2020

Answer Graph: Factorization Matters in Large Graphs

Our answer-graph method to evaluate SPARQL conjunctive queries (CQs) finds a factorized answer set first, an answer graph, and then finds the embedding tuples from this. This approach can reduce greatly the cost to evaluate CQs. This affords a second advantage: we can construct a cost-based planner. We present the answer-graph approach, and overview our prototype system, Wireframe. We then offer proof of concept via a micro-benchmark over the YAGO2s dataset with two prevalent shapes of queries, snowflake and diamond. We compare Wireframe's performance over these against PostgreSQL, Virtuoso, MonetDB, and Neo4J to illustrate the performance advantages of our answer-graph approach.

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arXiv2020

GGDs: Graph Generating Dependencies

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.

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arXiv2015

Towards Query Optimization for SPARQL Property Paths

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 - a waveguide plan (WGP) - which guides the query evaluation. There are numerous choices in the construction of a plan, and a number of optimization methods, meaning 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. We illustrate the types of optimizations this approach affords and the performance gains that can be obtained. A WGP's costs can be estimated, which opens the way to cost-based optimization.

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