Named property graphs Dominik Tomaszuk, ukasz Szeremeta 2019 - - PowerPoint PPT Presentation

named property graphs
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Named property graphs Dominik Tomaszuk, ukasz Szeremeta 2019 - - PowerPoint PPT Presentation

Named property graphs Dominik Tomaszuk, ukasz Szeremeta 2019 University of Bialystok, Institute of Informatics Introduction We introduce Named Property Graph model that allows to group graphs into separate units and describe information about


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Named property graphs

Dominik Tomaszuk, Łukasz Szeremeta 2019 University of Bialystok, Institute of Informatics

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SLIDE 2

Introduction

We introduce Named Property Graph model that allows to group graphs into separate units and describe information about them. We also present Cyphern query language that supports our proposal, mapping algorithms and use cases.

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Named property graph usage

access control information usage control data syndication graph signing aggregation or encapsulation of graph elements expressing propositional attitudes

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Named property graph

  • ability to name graph
  • graph can have own properties

○ graph name ○ properties for whole graph

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Cyphern

  • added FROM clause which specify name of property

graph

○ named property graph query language ○ additional FROM clause

MATCH (n) FROM (m) RETURN n, m

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SLIDE 6

Interoperability

  • easy apply for existing databases
  • mapping algorithms

○ named property graphs ➡ property graphs ○ Cyphern ➡ openCypher

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Mapping Named Property Graph into Property Graph

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Mapping Cyphern into

  • penCypher

MATCH (n)<-[:RELATED]-(m) RETURN n, m MATCH (n) FROM (m) RETURN n, m

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SLIDE 9
  • Java open-source Command Line Interface (CLI) SDF parser
  • parse chemical SDF files into 4 formats including openCypher
  • ability to add additional atoms data from periodic table
  • ability to change database IDs to full URLs

Available on GitHub

https://github.com/lszeremeta/SDFEater

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SLIDE 10

Conclusions

  • We present a Named Property

Graph model which allows to group graphs into separate units and describe information about them.

  • We also introduce Cyphern query

language that supports our proposal.

  • We also present mapping

algorithms, use cases with the chemical data, and SDFEater that is our tool for processing data.

  • Our proposal can be easily applied

to existing databases.

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Thank you!

Łukasz Szeremeta 2019

l.szeremeta@uwb.edu.pl