1. 23rd International World Wide Web Conference, 10th April 2014, Seoul, Korea
TripleProv
Efficient Processing of Lineage
Queries over a Native RDF Store
Marcin Wylot1
,
Philippe Cudré-Mauroux1
, and Paul Groth2
1)
eXascale Infolab, University of Fribourg, Switzerland
2)
Web & Madia Group, VU University Amsterdam, Netherlands

2. Outline
➢ Motivation
➢ Provenance Polynomials
➢ System
➢ Results

3. Data Provenance
“Provenance is information about
entities, activities, and people involved
in producing a piece of data or thing, which can be used to form
assessments about its quality, reliability or trustworthiness.”
How a query answer was derived: what data was
combined to produce the result.

4. Data Integration
➢ Integrated and summarized data
➢ Trust, transparency, and cost
➢ Capability to pinpoint the exact
source from which the result was
selected
➢ Capability to trace back the
complete list of sources and how
they were combined to deliver a
result

5. Querying Distributed Data Sources
How exactly was the answer derived?

6. Application: Post-query Calculations
➢ Scores or probabilities for query result
➢ Result ranking
➢ Compute trust
➢ Information quality based on used sources

7. Application: Query Execution
➢ Modify query strategies on the fly
➢ Restrict results to certain subset of sources
➢ Restrict results w.r.t. queries over provenance
➢ Access control, only certain sources will appear
➢ Detect if result would be valid when removing certain
source

8. Provenance Polynomials
➢ Ability to characterize ways each source contributed
➢ Pinpoint the exact source to each result
➢ Trace back the list of sources the way they were combined
to deliver a result

9. Graph-based Query
select ?lat ?long ?g1 ?g2 ?g3 ?g4
where {
graph ?g1 {?a [] "Eiffel Tower" . }
graph ?g2 {?a inCountry FR . }
graph ?g3 {?a lat ?lat . }
graph ?g4 {?a long ?long . }
}
lat long l1 l2 l4 l4,
lat long l1 l2 l4 l5,
lat long l1 l2 l5 l4,
lat long l1 l2 l5 l5,
lat long l1 l3 l4 l4,
lat long l1 l3 l4 l5,
lat long l1 l3 l5 l4,
lat long l1 l3 l5 l5,
lat long l2 l2 l4 l4,
lat long l2 l2 l4 l5,
lat long l2 l2 l5 l4,
lat long l2 l2 l5 l5,
lat long l2 l3 l4 l4,
lat long l2 l3 l4 l5,
lat long l2 l3 l5 l4,
lat long l2 l3 l5 l5,
lat long l3 l2 l4 l4,
lat long l3 l2 l4 l5,
lat long l3 l2 l5 l4,
lat long l3 l2 l5 l5,
lat long l3 l3 l4 l4,
lat long l3 l3 l4 l5,
lat long l3 l3 l5 l4,
lat long l3 l3 l5 l5,

10. TripleProv Resuls
result:
lat, long
provenance polynomial:
(l1 ⊕ l2 ⊕ l3) ⊗ (l4 ⊕ l5) ⊗ ( l6 ⊕ l7) ⊗ (l8 ⊕ l9)

11. Polynomials Operators
➢ Union (⊕)
○ constraint or projection satisfied with multiple sources
l1 ⊕ l2 ⊕ l3
○ multiple entities satisfy a set of constraints or projections
➢ Join (⊗)
○ sources joined to handle a constraint or a projection
○ OS and OO joins between few sets of constraints
(l1 ⊕ l2) ⊗ (l3 ⊕ l4)

12. Example Polynomial
select ?lat ?long where {
?a [] ``Eiffel Tower''.
?a inCountry FR .
?a lat ?lat .
?a long ?long .
}
(l1 ⊕ l2 ⊕ l3) ⊗ (l4 ⊕ l5) ⊗ ( l6 ⊕ l7) ⊗ (l8 ⊕ l9)

13. Example Polynomial
select ?l ?long ?lat where
{
?p name ``Krebs, Emil'' .
?p deathPlace ?l .
?c [] ?l .
?c featureClass P .
?c inCountry DE .
?c long ?long .
?c lat ?lat .
}
[(l1 ⊕ l2 ⊕ l3) ⊗ (l4 ⊕ l5)]
⊗
[( l6 ⊕ l7) ⊗ (l8) ⊗ (l9 ⊕ l10) ⊗ (l11 ⊕ l12) ⊗ (l13)]

14. Granularity Levels
➢ source-level: sources of a triples
➢ triple-level: all pieces of data used to answer the query
(l1 ⊕ l2) ⊗ (l3 ⊕ l4)

15. System Architecture

16. Native Data Model
➢ Semantically co-located data
➢ Template based molecules

17. Various Physical Storage Models
Differences:
➢ ease of implementation
➢ memory consumption
➢ query execution
➢ interference with the original concept of molecule
1) SPOL 2) LSPO 3) SLPO 4) SPLO

18. Annotated Triples
➢ Annotated provenance
➢ Quadruples
➢ Easy to implement
➢ Source data repeated
for each triple

19. Co-located Elements
➢ Data grouped by source
➢ Physically co-located
➢ Avoids duplication of the
same source inside a
molecule
➢ Data about a given subject
co-located in one molecule
➢ More difficult to implement

20. Experiments
How expensive it is to trace
provenance?
What is the overhead on query
execution time?

21. Datasets
➢ Two collections of RDF data gathered from the Web
○ Billion Triple Challenge (BTC): Crawled from the linked
open data cloud
○ Web Data Commons (WDC): RDFa, Microdata
extracted from common crawl
➢ Typical collections gathered from multiple sources
➢ sampled subsets of ~110 million triples each; ~25GB each

22. Workloads
➢ 8 Queries defined for BTC
○ T. Neumann and G. Weikum. Scalable join processing on very large rdf
graphs. In Proceedings of the 2009 ACM SIGMOD International
Conference on Management of data, pages 627–640. ACM, 2009.
➢ Two additional queries with UNION and OPTIONAL
clauses
➢ 7 various new queries for WDC
http://exascale.info/tripleprov

23. Results
Overhead of tracking provenance compared to
vanilla version of the system for BTC dataset
source-level co-located
source-level annotated
triple-level co-located
triple-level annotated

24. Conclusions
➢ provenance overhead is considerable but acceptable,
on average about 60-70%
➢ most suitable storage model depends upon data and
workloads characteristics
➢ annotated: more appropriate for heterogenous datasets
and workloads retrieving provenance
➢ co-located: more appropriate for homogenous datasets
and workload filtering by source

25. Future Work
➢ Distributed version
➢ Dynamic storage model
➢ Adaptive query execution strategies
➢ PROV output
➢ Over provenance queries

26. Summary
➢ TripleProv: an efficient triplestore tracking provenance
➢ Two storage models
➢ Fine-grained multilevel provenance tracing
➢ Formal provenance polynomials
➢ Experimental evaluation
http://exascale.info/tripleprov

27. Loading & Memory
Billion Triple Challenge
Web Data Commons

28. Results
Overhead of tracking provenance compared to
vanilla version of the system for WDC dataset
source-level SLPO
source-level SPOL
triple-level SLPO
triple-level SPOL

29. Polynomials: multiple records
[(l1 ⊕ l2 ⊕ l3) ⊗ (l4 ⊕ l5) ⊗ ( l6 ⊕ l7) ⊗ (l8 ⊕ l9)]
⊕
[(l5 ⊕ l7) ⊗ (l4) ⊗ ( l13 ⊕ l17) ⊗ (l28)]
⊕
[(l4) ⊗ (l1 ⊕ l2) ⊗ ( l3 ⊕ l7) ⊗ (l8 ⊕ l9⊕ l4)]