The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead.

1. 24th International World Wide Web Conference, 21st May 2015, Florence, Italy
Executing Provenance-Enabled
Queries over Web Data
Marcin Wylot1
,
Philippe Cudré-Mauroux1
, and Paul Groth2
1)
eXascale Infolab, University of Fribourg, Switzerland
2)
Elsevier Labs, Amsterdam, Netherlands

2. Research Question
How RDF databases can
efficiently support
provenance-enabled
queries?
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3. Outline
➢ Motivation
➢ Provenance-Enabled Queries
➢ Query Execution Strategies
➢ Results
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4. 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.”
Which pieces of data were combined to produce
the result?
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5. Data Integration
➢ Integrated and summarized data
➢ Trust, transparency, and cost
➢ Capability to store and track
provenance data (WWW 2014)
➢ Capability to tailor queries with
provenance information (WWW
2015)
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6. Querying Linked Data
use data following a provenance specification
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7. Provenance-Enabled Query
A Workload Query is a query producing results a user is
interested in. These results are referred to as workload
query results.
A Provenance Query is a query that selects a set of data
from which the workload query results should originate.
A Provenance-Enabled Query is a pair consisting of a
Workload Query and a Provenance Query, producing results
a user is interested in (as specified by the Workload Query)
and originating only from data pre-selected by the
Provenance Query.
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8. Provenance-Enabled Query: Example
SELECT ?t WHERE {
?a <type> <article> .
?a <tag> <Obama> .
?a <title> ?t . }
➢ ensure that the articles come from sources attributed to the government
SELECT ?ctx WHERE {
?ctx prov:wasAttributedTo <government> . }
➢ ensure that the data used to produce the answer was associated a
“SeniorEditor” and a “Manager”
SELECT ?ctx WHERE {
?ctx prov:wasGeneratedBy <articleProd>.
<articleProd> prov:wasAssociatedWith ?ed .
?ed rdf:type <SeniorEdior> .
<articleProd> prov:wasAssociatedWith ?m .
?m rdf:type <Manager> . }
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9. Executing Provenance-Enabled Queries
A workload and a provenance query are given as input to
a triplestore, which produces results for both queries and
then combine them to obtain the final results.
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10. TripleProv: Query Execution Pipeline
input: provenance-enable query
➢ execute the provenance query
➢ optionally pre-materializing or co-locating data
➢ optionally rewrite the workload queries
➢ execute the workload queries
➢
output: the workload query results, restricted to those which were derived
from data specified by the provenance query 10

11. Physical Storage Models
A molecule collocates objects related
to a given subject; it is composed of a
subject, and a series of predicate and
object related to that subject.
Extended for provenance data a
molecule collocates the context values
with the predicate-object pairs.
This avoids the duplication of the
same context value, while at the same
time collocating all data about a given
subject in one structure.
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RDF molecule
basic data unit

12. Query Execution Strategies
1. Post-Filtering
2. Query Rewriting
3. Full Materialization
4. Pre-Filtering
5. Partial Materialization
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13. Post-Filtering
➢ the baseline strategy
➢ executes both the workload and the provenance query independently.
➢ the provenance and workload queries can be executed in any order
➢ the results from the provenance query are used to filter a posteriori
the results of the workload query based on their provenance
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14. Query Rewriting
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execute the provenance
query
rewrite the query plan;
add provenance
constraints
return restricted results
➢ efficient from the provenance query
execution side
➢ can be suboptimal from the
workload query execution side
It can be implemented in two ways by
the triplestores, either by modifying
the query execution process, or by
rewriting the workload queries in
order to include constraints on the
named graphs.

15. Full Materialization
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We implemented a basic view mechanisms in TripleProv. These
mechanisms allow us to project, materialize and utilize as a
secondary structure the portions of the molecules that are following
the provenance specification.

16. Full Materialization
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execute the
provenance query
materialize data for
the provenance query
execute workload
queries on the
materialized view
➢ This strategy will outperform all other
strategies when executing the workload
queries, since they are executed as is on
the relevant subset of the data.
➢ Materializing all potential tuples based on
the provenance query can be expensive,
both in terms of storage space and latency.
➢ Implementing this strategy requires either to
manually materialize the relevant tuples and
modify the workload queries accordingly, or
to use a triplestore supporting materialized
views.

17. Pre-Filtering
➢ Dedicated provenance index
collocates, for each context
values, the ids (or hashes) of all
tuples belonging to this context.
➢ The index is created upfront
when the data is loaded.
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18. Pre-Filtering
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execute the
provenance query
execute workload
queries, including
early filtering with the
provenance index
➢ The provenance index is
looked up during the query
execution to filter molecules
that are compatible with the
provenance specification.
➢ This strategy requires to create
a new index structure in the
system, and to modify both the
loading and the query execution
processes.

19. Partial Materialization
➢ This strategy introduces a trade-off between the performance of
the provenance query and that of the workload queries.
➢ While executing the provenance query, the system builds a
temporary structure maintaining the ids of all molecules
belonging to the context values returned by the provenance
query.
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20. Partial Materialization
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execute the
provenance query and
partially materialize
molecules
execute workload
queries, including
early filtering based
on pre-materialized
set of molecules
➢ The system dynamically (and efficiently)
looks-up all molecules and can filter
them out early in case they do not
appear in the temporary structure.
➢ Query processing operations can be
executed faster on a reduced number of
elements.
➢ The implementation of this strategy
requires the introduction of an additional
data structure at the core of the system,
and the adjustment of the query
execution process in order to use it.

21. Experiments
What is the most efficient query
execution strategy for provenance-
enabled queries?
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22. 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 ~40 million triples each; ~10GB each
➢ Added provenance specific triples (184 for WDC and 360 for BTC); that
the provenance queries do not modify the result sets of the workload
queries
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23. Workloads
➢ 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/provqueries
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24. Results for BTC
➢ Full Materialization: 44x faster
than the vanilla version of the
system
➢ Partial Materialization: 35x faster
➢ Pre-Filtering: 23x faster
➢ Adaptive Partial Materialization
executes a provenance query and
materialize data 475 times faster
than Full Materialization
➢ Query Rewriting and Post-
Filtering strategies perform
significantly slower
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25. Results for Representative Scenario
➢ original BTC dataset
➢ no added triples
➢ output changes due to
provenance specification
➢ higher performance gains
for all provenance aware
strategies are in the more
realistic scenario
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smaller number of context values from the provenance query
smaller number of relevant molecules to inspect

26. Data Analysis
➢ How many context values refer
to how many triples? How
selective it is?
➢ 6'819'826 unique context values
in the BTC dataset.
➢ The majority of the context
values are highly selective.
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➢ average selectivity
○ 5.8 triples per context value
○ 2.3 molecules per context value

27. Conclusions
➢ Querying provenance data does not necessarily
introduce a performance overhead.
➢ Queries tailored with provenance data can be executed
faster.
➢ Provenance information is highly selective.
➢ Partial Materialization represents the best trade-off for
provenance-enabled queries, but it introduces a
materialization cost and is not trivial to implement.
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28. Summary
➢ provenance-enabled queries: to tailor queries with
provenance information
➢ five provenance aware query execution strategies
➢ TripleProv: an efficient triplestore allowing to store, track,
and query provenance
➢ experimental evaluation and data analysis
★ http://exascale.info/provqueries
★ http://exascale.info/tripleprov
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❖ email: marcin@exascale.info
❖ twitter: @mwylot