Incremental Schema Discovery at Scale for RDF Data

Bouhamoum, Redouane; Kedad, Zoubida; Lopes, Stéphane

doi:10.1007/978-3-030-77385-4_12

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Thus, they are not designed to update the discovered schema for evolving graphs. Recently, Bouhamoum et al [12] proposed to use density-based clustering to extract schema information from an RDF graph and to incrementally update the schema when new RDF instances arrive. While the work can deal with additions, the deletion of edges and vertices is not considered.…”

Section: Related Workmentioning

confidence: 99%

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Blume

Richerby

Scherp

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Graph summarization is the task of finding condensed representations of graphs such that a chosen set of (structural) subgraph features in the graph summary are equivalent to the input graph. Existing graph summarization algorithms are tailored to specific graph summary models, only support one-time batch computation, are designed and implemented for a specific task, or evaluated using static graphs. Our novel, incremental, parallel algorithm addresses all these shortcomings. We support various structural graph summary models defined in our formal language FLUID. All graph summaries defined with FLUID can be updated in time O(∆ •d k), where ∆ is the number of additions, deletions, and modifications to the input graph, d is its maximum degree, and k is the maximum distance in the subgraphs considered. We empirically evaluate the performance of our algorithm on benchmark and real-world datasets. Our experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms their batch counterpart, even when about 50% of the graph database changes. The source code and the experimental results are openly available for reproducibility and extensibility.

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Section: Related Workmentioning

confidence: 99%

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Blume

Richerby

Scherp

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…Ours is a purely statistical approach (presented in detail in [2]), which we have experimentally shown to provide better accuracy and performance than previous analytical methods [7]. While schema discovery methods have been proposed for RDF datasets [4], notably based on distributed clustering using Spark, our method is centralized and supports the more expressive property graph model.…”

Section: Introductionmentioning

confidence: 99%

DiscoPG

et al. 2022

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Property graphs are becoming pervasive in a variety of graph processing applications using interconnected data. They allow to encode multi-labeled nodes and edges, as well as their properties, represented as key/value pairs. Although property graphs are widely used in several open-source and commercial graph databases, they lack a schema definition, unlike their relational counterparts. The property graph schema discovery problem consists of extracting the underlying schema concepts and types from such graph datasets. We showcase DiscoPG, a system for efficiently and accurately discovering and exploring property graph schemas. To this end, it leverages hierarchical clustering using a Gaussian Mixture Model, which accounts for both node labels and properties. DiscoPG allows users to perform schema discovery for both static and dynamic graph datasets. Suitable visualization layouts and dedicated dashboards enable the user perception of the static and dynamic inferred schema on the node clusters, as well as the differences in runtimes and clustering quality. To the best of our knowledge, DiscoPG is the first system to tackle the property graph schema discovery problem. As such, it supports the insightful exploration of the graph schema components and their evolving behavior, while revealing the underpinnings of the clustering-based discovery process.

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