Optimizing RDF Data Cubes for Efficient Processing of Analytical Queries

Aalborg University

Optimizing RDF Data Cubes for Efficient Processing ofAnalytical Queries

Kim Ahlstrøm JakobsenAlex B. Andersen

Katja HoseTorben Bach Pedersen

Database Technology,Department of Computer Science,

Aalborg University

Kim Ahlstrøm Jakobsen Optimizing RDF Data Cubes 1 / 19

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Motivation

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Motivation

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Motivation

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Motivation

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Motivation

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Motivation

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Future Goal

Analytical queries on internal data & external linked data

Benefits

Enables exploratory queries

Increasing amount of linked data

Integrates with heterogeneous data

Semantic reasoning

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Future Goal

Analytical queries on internal data & external linked data

Benefits

Enables exploratory queries

Increasing amount of linked data

Integrates with heterogeneous data

Semantic reasoning

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The First Steps

Efficient Processing of Analytical Querying on RDF Data Cubes.

Denormalize the cube dimensions

Reduce the subject-object joins (expensive)

Increase the subject-subject joins

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The First Steps

Efficient Processing of Analytical Querying on RDF Data Cubes.

Denormalize the cube dimensions

Reduce the subject-object joins (expensive)

Increase the subject-subject joins

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Workflow

Internal optimization

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Workflow

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Workflow

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Workflow

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Workflow

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Workflow

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Workflow

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Workflow

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Workflow

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Building the Cube

Purpose

Organize data with purpose ofanalysis

Easier to understand

What is a cube

Facts: The subject of the analysis

Dimensions: Perspectives of the data

Levels: Concepts in the dimensions

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Building the Cube

Purpose

Organize data with purpose ofanalysis

Easier to understand

What is a cube

Facts: The subject of the analysis

Dimensions: Perspectives of the data

Levels: Concepts in the dimensions

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Analytical Queries

Example Query 1

What is the revenue per country?

Example Query 2

What are the top k products bought by customers from Denmark?

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Analytical Queries

Example Query 1

What is the revenue per country?

Example Query 2

What are the top k products bought by customers from Denmark?

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PatternsSnowflake Pattern

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PatternsStar Pattern

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PatternsFully Denormalized Pattern

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Special Cases:Unbalanced Hierarchies

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Special Cases:Property Collision

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Semantic Web OLAP Denormalization Algorithm

QB4OLAP ontology

Snowflake pattern RDF datacube

Output

Star pattern RDF data cube

Fully Denormalized pattern RDFdata cube

Features

Top-down traversal

Property renaming

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Unbalanced Hierarchies Example

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Query rewriting

SELECT ?name sum(? p r i c e )WHERE {? l i n e i t em : e x t e n d e d p r i c e ? p r i c e ;

: h a s o r d e r ? o r d e r .? o r d e r sko s : b roade r ? customer .? customer sko s : b roade r ? na t i o n .? na t i o n : name ?name .

}GROUP BY ?name

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Query rewriting

: h a s o r d e r ? o r d e r .? o r d e r sko s : b roade r ? customer .? customer sko s : b roade r ? na t i o n .? na t i o n : name ?name .

}GROUP BY ?name

: h a s o r d e r ? o r d e r .? o r d e r : nat ion name ?name .

}GROUP BY ?name

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Results

Virtuoso

Star Denormalized

Increase in Triples 16 % 173 %Avg. Decease in Query Time 600 % 700 %Geo. M. Decease in Query Time 110 % 140 %

Cost of triple storage

Static and frequently changing data

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Future Work

More cube optimizations

Consider data provenance andquality

Thank you

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SWOD AbstractExample

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Figure Credits

Workman – Licence: CC BY 3.0Credit: www.clipartbest.com

Cube – Licence: CC BY 3.0Credit: www.clipartbest.com

Turing machinehttp://www.felienne.com/

Stepshttp://www.cliparthut.com/

Future-workhttp://www.horsesforsources.com/

Optimizing RDF Data Cubes for Efficient Processing of Analytical Queries

Data & Analytics

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