Given the heterogeneity of the data one can find on the Linked Data cloud, being able to trace back the provenance of query results is rapidly becoming a must-have feature of RDF systems. While provenance models have been extensively discussed in recent years, little attention has been given to the efficient implementation of provenance-enabled queries inside data stores. This paper introduces TripleProv: a new system extending a native RDF store to efficiently handle such queries. TripleProv implements two different storage models to physically co-locate lineage and instance data, and for each of them implements algorithms for tracing provenance at two granularity levels. In the following, we present the overall architecture of our system, its different lineage storage models, and the various query execution strategies we have implemented to efficiently answer provenance-enabled queries. In addition, we present the results of a comprehensive empirical evaluation of our system over two different datasets and workloads.
TripleProv: Efficient Processing of Lineage Queries over a Native RDF Store
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
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
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,
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)
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
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
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