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Communication Dans Un Congrès Année : 2024

Finding the PG schema of any (semi)structured dataset: a tale of graphs and abstraction

Nelly Barret
Tudor Enache
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  • PersonId : 1386704
Ioana Manolescu

Résumé

Property Graphs (PGs) are an attractive data model both for business users, and for developers of data management tools. They combine the internal structure helpful in relational databases, where each record has a clearly identified set of at- tributes, with the flexible structure and support for heterogeneity, common in graph databases. Several useful and/or interesting datasets are available in non-PG data models. These include legacy databases, created before the advent of the PG standards, as well as well-known benchmarks based on real and synthetic data, Open Data published in other formats such as XML, JSON or RDF, etc. Converting such datasets to Property Graphs would enable their exploitation under the PG model. In this work-in-progress paper, we describe an approach to derive, from any (semi)-structured dataset, a PG schema consisting of node types, edge types, and a graph type. Our approach builds on (i) ConnectionLens, a tool for converting (semi)- structured datasets into simple data graphs, and (ii) Abstra, which, in a ConnectionLens graph, identifies a set of entities and relationships. This work is the first step towards a universal data migration tool from (semi)-structured data, to PGs.
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Dates et versions

hal-04591933 , version 1 (29-05-2024)

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  • HAL Id : hal-04591933 , version 1

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Nelly Barret, Tudor Enache, Ioana Manolescu, Madhulika Mohanty. Finding the PG schema of any (semi)structured dataset: a tale of graphs and abstraction. SEAGraph Workshop 2024 - 3rd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores - 40th IEEE International Conference on Data Engineering (ICDE 2024), May 2024, Utrecht, Netherlands. ⟨hal-04591933⟩
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