Scalable Semantic Version Control for Linked Data Management (presented at 2nd Workshop on Linked Data Quality at ESWC 2015 in Portorož, Slovenia)

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This document proposes techniques for scalable semantic version control of linked data, including delta-based storage schemas and partial on-demand reconstruction of historic versions. It evaluates query optimization strategies for targeted queries across versions of large datasets with millions of triples and thousands of commits. Results show the optimized approach significantly outperforms a baseline, processing queries up to 100x faster by caching commit graphs and loading changed triples on demand.

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Technische Universität München
Scalable Semantic Version Control
for Linked Data Management
Claudius Hauptmann, Michele Brocco and Wolfgang Wörndl
Technische Universität München
2nd Workshop on Linked Data Quality
at ESWC 2015 (June 1st, 2015 - Portorož, Slovenia)

Technische Universität München
2
Version Control for Linked Data - Goal
• Scalable (reduce disk space-, CPU- and memory
consumption, network traffic and disk I/O)
• Semantic (data about versioning accessible via SPARQL
queries and OWL reasoners)

Technische Universität München
3
Query Types
• Cross-version queries ("Which triples were modified last
month that are related to Portorož?")
• Targeted queries ("What did we know about Portorož in
version X ?")

Technische Universität München
4
Update Types
• Creation of branches
• Modification of triples (working on a branch)
• Review and correction of modifications
• Commit of modifications (creation of a version)
• Merging of branches
• Deletion of branches

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5
Storage Strategies for Versioned Triples
• Version-based
• Delta-based
• Hybrid
• Hypergraph-based
• Partial Order Index

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6
Delta-Based Storage Schema

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7
Partial On-Demand Reconstruction of Historic Versions

Technische Universität München
8
Partial On-Demand Reconstruction of Historic Versions

Technische Universität München
9
Query Engine Optimization
• Arbitrary length path operator is inefficient (many lookup
operations)
• Replace slow operators by new operators, that are
optimized for partial on-demand version reconstruction
• Use in-memory indices for commits
• Cache indices

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Step 1: Loading commits into in-memory index
• Caching for queries on same graph

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Step 2: Planning Commit Graph Traversal
• Caching for queries on same graph and same version

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Step 3: Loading changes for chunk of triples
• Load relationships between triples and commits:
• Commit-triple index
• Triple-commit index

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Step 4: Traverse Commit Graph + Test Triples
• Start traversal at commit specified in query
• Get changes for commit from index
• Check if add or delete
• Save result for triple
• Remove triple from indices
• If indices not empty go to next commit and repeat

Technische Universität München
14
Evaluation - Dataset
• DBpedia class assertions (version 2014)
• 28,031,852 triples
• 3 datasets: 280,319, 28,032 and 2,804 commits (triples
equally distributed over commits, 1 branch, no deletes)
• Base line: query with 3 arbitrary length path operators
• Repeated 100x (base line 10x), no caching
• Test queries:

Technische Universität München
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Evaluation - Response Time Query 1 (7 results)
#commits 2,804 28,032 280,319
Baseline 30,537 ms 304,474 ms 3,061,018 ms
Optimized 126 ms 318 ms 3,110 ms
Loading commits 15.15 ms 162.5 ms 2,352 ms
Creating Plan 0.65 ms 11.6 ms 178 ms
Loading changes 14.87 ms 15.6 ms 16 ms
Traversal 0.39 ms 4.7 ms 60 ms

Technische Universität München
16
Evaluation - Response Time Query 2 (3108 results)
#commits 2,804 28,032 280,319
Baseline 57.009 ms 607.924 ms OutOfMemoryError
Optimized 792 ms 1,188 ms 5,910 ms
Loading commits 10.95 ms 160.6 ms 2,326 ms
Creating Plan 0.60 ms 11.9 ms 175 ms
Loading changes 610.57 ms 616.5 ms 649 ms
Traversal 13.54 ms 160.6 ms 2,000 ms

Technische Universität München
17
Conclusion and Outlook
• Conclusion: Delta-based storage strategies can be used
for datasets with millions of triples and thousands of
versions for targeted queries by partial on-demand
version reconstruction and query engine optimization
• Outlook:
• Optimization of implementation
• Evaluation with established benchmarks
• Evaluation of caching strategies
• Evaluation with hybrid storage strategies
• Integration into existing systems (e.g. r43ples)

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1. Technische Universität München Scalable Semantic Version Control for Linked Data Management Claudius Hauptmann, Michele Brocco and Wolfgang Wörndl Technische Universität München 2nd Workshop on Linked Data Quality at ESWC 2015 (June 1st, 2015 - Portorož, Slovenia)

2. Technische Universität München 2 Version Control for Linked Data - Goal • Scalable (reduce disk space-, CPU- and memory consumption, network traffic and disk I/O) • Semantic (data about versioning accessible via SPARQL queries and OWL reasoners)

3. Technische Universität München 3 Query Types • Cross-version queries ("Which triples were modified last month that are related to Portorož?") • Targeted queries ("What did we know about Portorož in version X ?")

4. Technische Universität München 4 Update Types • Creation of branches • Modification of triples (working on a branch) • Review and correction of modifications • Commit of modifications (creation of a version) • Merging of branches • Deletion of branches

5. Technische Universität München 5 Storage Strategies for Versioned Triples • Version-based • Delta-based • Hybrid • Hypergraph-based • Partial Order Index

6. Technische Universität München 6 Delta-Based Storage Schema

7. Technische Universität München 7 Partial On-Demand Reconstruction of Historic Versions

8. Technische Universität München 8 Partial On-Demand Reconstruction of Historic Versions

9. Technische Universität München 9 Query Engine Optimization • Arbitrary length path operator is inefficient (many lookup operations) • Replace slow operators by new operators, that are optimized for partial on-demand version reconstruction • Use in-memory indices for commits • Cache indices

10. Technische Universität München 10 Step 1: Loading commits into in-memory index • Caching for queries on same graph

11. Technische Universität München 11 Step 2: Planning Commit Graph Traversal • Caching for queries on same graph and same version

12. Technische Universität München 12 Step 3: Loading changes for chunk of triples • Load relationships between triples and commits: • Commit-triple index • Triple-commit index

13. Technische Universität München 13 Step 4: Traverse Commit Graph + Test Triples • Start traversal at commit specified in query • Get changes for commit from index • Check if add or delete • Save result for triple • Remove triple from indices • If indices not empty go to next commit and repeat

14. Technische Universität München 14 Evaluation - Dataset • DBpedia class assertions (version 2014) • 28,031,852 triples • 3 datasets: 280,319, 28,032 and 2,804 commits (triples equally distributed over commits, 1 branch, no deletes) • Base line: query with 3 arbitrary length path operators • Repeated 100x (base line 10x), no caching • Test queries:

15. Technische Universität München 15 Evaluation - Response Time Query 1 (7 results) #commits 2,804 28,032 280,319 Baseline 30,537 ms 304,474 ms 3,061,018 ms Optimized 126 ms 318 ms 3,110 ms Loading commits 15.15 ms 162.5 ms 2,352 ms Creating Plan 0.65 ms 11.6 ms 178 ms Loading changes 14.87 ms 15.6 ms 16 ms Traversal 0.39 ms 4.7 ms 60 ms

16. Technische Universität München 16 Evaluation - Response Time Query 2 (3108 results) #commits 2,804 28,032 280,319 Baseline 57.009 ms 607.924 ms OutOfMemoryError Optimized 792 ms 1,188 ms 5,910 ms Loading commits 10.95 ms 160.6 ms 2,326 ms Creating Plan 0.60 ms 11.9 ms 175 ms Loading changes 610.57 ms 616.5 ms 649 ms Traversal 13.54 ms 160.6 ms 2,000 ms

17. Technische Universität München 17 Conclusion and Outlook • Conclusion: Delta-based storage strategies can be used for datasets with millions of triples and thousands of versions for targeted queries by partial on-demand version reconstruction and query engine optimization • Outlook: • Optimization of implementation • Evaluation with established benchmarks • Evaluation of caching strategies • Evaluation with hybrid storage strategies • Integration into existing systems (e.g. r43ples)

Scalable Semantic Version Control for Linked Data Management (presented at 2nd Workshop on Linked Data Quality at ESWC 2015 in Portorož, Slovenia)

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