=Paper=
{{Paper
|id=None
|storemode=property
|title=How to benchmark RDF engines and not die trying
|pdfUrl=https://ceur-ws.org/Vol-666/paper6.pdf
|volume=Vol-666
|dblpUrl=https://dblp.org/rec/conf/semweb/LampoMRV10
}}
==How to benchmark RDF engines and not die trying==
https://ceur-ws.org/Vol-666/paper6.pdf
How to Benchmark RDF Engines and Not Die Trying
Tomas Lampo1 , Amadı́s Martı́nez2,3 , Edna Ruckhaus2 and Marı́a-Esther Vidal2
1
University of Maryland, College Park, USA
tlampo@cs.umd.edu
2
Universidad Simón Bolı́var, Caracas, Venezuela
{amadis,ruckhaus,mvidal}@ldc.usb.ve
3
Universidad de Carabobo, Valencia, Venezuela
Abstract. Existing RDF engines have developed optimization techniques and
efficient physical operators that speed up execution time. Additionally, some of
these approaches have implemented structures to efficiently store and access RDF
data, and have developed execution strategies able to reuse data previously stored
in cache. In order to study the benefits of the techniques offered by each ap-
proach, particular datasets have to be considered as well as different benchmarks
of queries. Queries must be of different complexity, number of patterns, selectiv-
ity and shape, while datasets have to be of different sizes and correlations between
subjects, predicates and objects. In this paper, we describe the benchmarks that
we have developed to analyze the performance of existing RDF engines when
creating and indexing RDF structures, as well as the quality of the optimization
and execution strategies implemented by these engines.
1 Introduction
In the context of the Semantic Web, several query engines have been developed to
access RDF documents efficiently [3, 10, 11, 13, 14, 18, 22, 25]. The majority of these
approaches have implemented optimization techniques and developed execution en-
gines on top of effective physical operators and accurate estimators [3, 15, 18, 22]. Ad-
ditionally, some of these approaches have implemented structures to efficiently store
and access RDF data, and have developed execution strategies able to reuse data previ-
ously stored in the cache. To evaluate the performance of the implemented techniques,
datasets and queries with specific properties must be taken into account [9]. Queries
should be of different complexity, number of patterns, selectivity and shape. Datasets
ought to be of different sizes and correlations between subjects, predicates and objects;
also, they can be characterized according to the number of classes, properties, axioms,
hierarchies or instances.
In this paper, we aim at the characterization of benchmarks that exhibit the perfor-
mance of existing RDF engines. We are particularly interested in identifying the type
of datasets that can be used to analyze the efficiency and effectiveness of loading and
indexing procedures, as well as the properties of the queries that can stress existing
optimization and query execution techniques. Thus, more than proposing a benchmark,
our purpose is to illustrate how issues such as the size and correlations between RDF
data, the shape of query plans, and the selectivity of intermediate join results, can reveal
important characteristics of existing RDF engines.
Proceedings of the International Workshop on Evaluation of Semantic Technologies (IWEST 2010). Shanghai, China. November 8, 2010.
First, the dataset size is a relevant issue in experiments where the load and index
time are analyzed; thus, we consider synthetic datasets like LUBM [7], or real-world
datasets like YAGO 4 and government data. YAGO can be classified as a large dataset,
LUBM can be generated with an increasing number of concepts, while government data
repositories can be extended by considering different years and RDF properties. Thus,
these datasets can be used to evaluate RDF engines’ scalability, i.e., how much the load
time is affected when the dataset size increases.
Furthermore, queries that can be partitioned into large numbers of star shaped groups,
can reveal an interesting behavior of state-of-the-art optimization and execution tech-
niques. Additionally, queries that produce a very large number of intermediate results
or require CPU-intensive processing, can show the efficacy of cache management tech-
niques and physical operators. In this paper, we characterize some significant properties
of the benchmarks, illustrate the results that we have obtained by exploiting them in our
empirical studies, and motivate the consideration of these issues during benchmarking.
To summarize, the main contributions of this paper are the following:
– The characterization of a family of query plans that can benefit from warming
up cache. These queries reduce the number of intermediate results and CPU pro-
cessing, and can be rewritten as bushy plans comprised of small-sized star-shaped
groups.
– Benchmarks comprised of queries and plans that show how a query plan shape can
impact on an RDF query engine performance.
– An empirical study of the RDF engines’ performance in terms of load and indexing
time, and query optimization and execution time.
This paper is comprised of four additional sections. Section 2 describes the main
properties of existing RDF engines and benchmark frameworks; section 3 presents the
characteristics of the developed benchmarks. Section 4 presents an experimental study
where we report on the performance of state-of-the-art RDF engines. Finally, we con-
clude in section 5 with an outlook to future work.
2 Existing RDF Engines and Benchmark Frameworks
During the last years, several query engines have been developed to access RDF data [3,
10, 11, 13, 14, 18, 25]. Jena [13, 26] provides a programmatic environment for SPARQL;
it includes the ARQ query engine and indices, which provide an efficient access to
large datasets. Tuple Database or TDB [14] is a persistent graph storage layer for Jena;
it works with the Jena SPARQL query engine (ARQ) to support SPARQL together
with a number of extensions (e.g., property functions, arbitrary length property paths).
Sesame [25] is an open source Java framework for storing and querying RDF data; it
supports SPARQL and SeRQL queries, which are translated to Prolog. These engines
have not been defined to scale up to large datasets or complex queries, so benchmarks
comprised of queries of different complexity and large datasets could be used to stress
these engines’ performance.
4
Ontology available for download at http://www.mpi-inf.mpg.de/yago-naga/yago/
Additionally, different storage and access structures have been proposed to effi-
ciently retrieve RDF data [6, 17, 23, 24]. Hexastore [24] is a main memory indexing
technique that exploits the role of the arguments of an RDF triple; six indices are de-
signed so that each one can efficiently retrieve a different access pattern. A secondary-
memory-based solution for Hexastore has been presented in [23]; this solution scales up
to larger datasets, but because the same object may be stored in several indices, memory
can be used inefficiently. AllegroGraph [3] uses a native object store for on-disk binary
tree-based storage of RDF triples. AllegroGraph also maintains six indices to manage
all the possible permutations of subject (s), predicate (p) and object (o). The standard
indexing strategy is to build indices whenever there are more than a certain number of
triples. Fletcher et al. [6] propose indexing the universe of RDF resource identifiers,
regardless of the role played by the resource; although they are able to reduce the stor-
age costs of RDF documents, since the proposed join implementations are not closed
with respect to the index structures, the properties of these structures can only be ex-
ploited in joins on basic graph patterns. In addition to query complexity and dataset size
that affect the performance of these engines, another issue that needs to be considered
in benchmarks for these engines, is the correlation between the values of the subjects,
predicates and objects; this property can help to impact on the load and index time.
MacGlothlin et al. [16] propose an index-based representation for RDF documents
that materializes the results for subject-subject joins, object-object joins and subject-
object joins. This approach has been implemented on top of MonetDB [12] and it can
exploit the Monet DB cache management system. Recently, Atre et al. [4] proposed the
BitMat approach which is supported on a fully inverted index structure that implements
a compressed bit-matrix structure of the RDF data. An RDF engine has been developed
on top of this bit-based structure, which exploits the properties of this structure and
avoids the storage of intermediate results generated during query evaluation. Although
these structures can speed up the evaluation of joins, this solution may not scale up to
very large strongly connected RDF graphs. Thus, to analyze the performance of these
approaches, dataset size and correlation between the values of the subjects, predicates
and objects have to be set up during benchmark configuration.
Abadi et al. [1, 2] and Sidirourgos et al. [21] propose different RDF storage schemas
to implement an RDF management system on top of a relational database system. They
empirically show that a physical implementation of vertically partitioned RDF tables
may outperform the traditional physical schema of RDF tables. In addition, any of these
solutions can exploit the properties of the database manager to efficiently manage the
cache. RDF-3X [18] focuses on an index system, and has implemented optimization and
evaluation techniques that support an efficient and scalable evaluation of RDF queries.
RDF-3X optimizer relies on a dynamic-based programming algorithm that is able to
identify left linear plans comprised of star-shaped groups; however, this optimizer does
not scale up to complex queries. In addition, RDF-3X is able to load in resident memory
portions of data, and thus differences between execution time in both cold and warm
cache can be observed for certain types of queries. Additionally to the properties of
the benchmarks previously described, the shape of the queries and the selectivity of the
intermediate joins, should be considered in the benchmarks.
Furthermore, in the context of the Semantic Web, benchmarking has motivated
the evaluation of these query engines, and contributed to improve scalability and per-
formance [9]. Among the most used benchmarks, we can mention: LUBM [8], the
Berlin SPARQL Benchmark [5], the RDF Store Benchmarks with DBpedia5 , and the
SP2 Bench SPARQL benchmark [20].
The LUBM benchmark has been defined to compare performance, completeness
and soundness of OWL reasoning engines. This allows us to generate ontologies of dif-
ferent sizes and expressiveness, and also provides a set of queries; so performance and
scalability of reasoning tasks such as subsumption, realization and transitive closure,
can be evaluated. The Berlin SPARQL Benchmark is settle in an e-commerce domain,
and it has been designed to test storage workloads and the performance SPARQL end-
points on large datasets; properties, depth and width of the subsumption hierarchies
can be configured, as well as queries that emulate search and navigation patterns in
the generated datasets. The SP2 Bench SPARQL benchmark generates DBLP-like RDF
documents, and a set of SPARQL queries that cover a large number of SPARQL opera-
tors whose performance can be studied in different RDF engines. Finally, the RDF Store
Benchmarks with DBpedia provides a set of SPARQL queries to be executed against
DBpedia and provides the basis to test the performance of existing RDF engines when
small portions of a large dataset are required to execute a query.
Additionally, RDF engines’ authors have conducted empirical studies with selected
datasets and tailored queries to exhibit the engines’ performance [4, 16, 19, 22]. Atre
et al. [4] chose UniProt and a synthetic LUBM dataset with 10,000 universities to
stress the BitMat storage capabilities; in addition, previously published queries, were
adapted to reveal the performance of the BitMat query processing algorithms in com-
plex join queries with low-selectivity intermediate results. Neumann et al. [19] used
two large datasets to exhibit storage workload properties, and a set of queries comprised
of chained triple patterns to exploit performance of warm caches, physical join opera-
tors, and selectivity estimation techniques; although the queries are composed of up to
14 triple patterns, they are not complex enough to stress the optimizer or to diminish
warm cache performance.
Similarly to existing benchmarks, we tailored a family of queries that allow us to
reveal the performance of existing RDF engines; however, we focus on illustrating the
impact of the shape of query plans on the performance of warm caches and physical
join operators.
3 Analysis of the Benchmark Characteristics
Several factors are considered for query benchmarks: the number of patterns in each
query, the number of instantiated patterns, the number of answers (including no-answer
queries), the number of intermediate triples that are produced during its evaluation, and
the query groupings together with their size and shape, e.g., bushy plans comprised
of small-sized star-shaped groups. All of these features are adjusted according to the
properties of the RDF engines on which the experimental study is being developed.
5
http://www4.wiwiss.fu-berlin.de/benchmarks-200801/
For example, RDF-3X implements a dynamic-based query optimizer that does not eas-
ily scale-up to queries with a large number of sub-goals or star-shaped join binding
patterns6 , so query size and shape should be considered. Additionally, RDF-3X offers
cache techniques for an efficient execution, so query benchmarks must be comprised of
queries that produce a large number of intermediate results that require CPU-intensive
processing.
Furthermore, we have developed extensions to some of the existing RDF query
engines, this is the case of GRDF-3X and GJena, which implement a version of the
gjoin operator that we have defined to combine small-sized star-shaped groups in a
bushy plan; for these extensions it is important to define benchmarks that contain bushy
plans comprised of small-sized star-shaped groups [22].
The influence of the benchmark factors on the RDF engine features is illustrated in
Table 1.
Table 1. Relationship between benchmark factors and RDF engine features
benchmark cache query physical indexing
factor management optimization operators structures
# patterns X X
# pattern instantiations X X
# small star-shaped groups X X X
# intermediate triples X X
# number of answers X
different instantiation values X
RDF engines may implement different types of optimization algorithms. Some en-
gines implement greedy algorithms that explore a limited number of query plans. When
the number of patterns increases, the optimization effectiveness may be compromised.
Other engines implement dynamic programming-based algorithms that only explore
linear plans; so performance of these algorithms may be degraded when the number
of query patterns is large or the join binding pattern is not a chain. Randomized op-
timization algorithms, when properly configured, may efficiently identify bushy query
execution plans; however, temperature and number of iterations have to be adjusted.
Queries that generate a large number of intermediate results will not benefit from
warming the cache because of frequent page faults. On the other hand, bushy plans
comprised of small-sized star-shaped group may produce intermediate join results that
fit in memory and could be reused in further join operations; thus, cold cache times can
be reduced for these types of queries.
Indexing structures improve triple search on subject, property or object specific
values. Therefore, index structures are better exploited when patterns are instantiated.
Also, the number of answers affects the performance of RDF engines that can detect if
the query answer is empty; so queries that produce empty answers are also required.
6
Queries comprised of star-shaped join binding patterns can be shaped as bushy plans composed
of star-shaped groups.
Finally, query optimization may be affected by dependencies among property val-
ues and non-uniform distribution of these values. Thus, query optimization that relies
on cost estimates that do not take into account these dependencies, may take wrong
decisions based on imprecise estimates. Thus, benchmarks that contain queries with
same patterns, orderings and groupings but different instantiations of dependent values,
should be also considered.
4 Experimental Study
Considering the characteristics described in previous sections, we have defined several
benchmarks and conducted extensive experimental studies. We considered the follow-
ing RDF engines: RDF-3X versions 0.3.3 and 0.3.4, AllegroGraph RDFStore version
3.0.1, Jena version 2.5.7, Jena with TDB version 0.7.3., and GRDF-3X7 built on top of
RDF-3X version 0.3.4. We studied efficiency and effectiveness of the load and index-
ing processes, as well as the optimization and evaluation techniques of these engines;
additionally, we studied the effects of query shape in cache usage.
Dataset Number of triples N3-Size
LUBM Univ(1, 0) 103,074 17.27 MB
LUBM Univ(5, 0) 645,649 108.28 MB
LUBM Univ(10, 0) 1,316,322 220.79 MB
LUBM Univ(20, 0) 2,781,322 468.68 MB
LUBM Univ(50, 0) 6,888,642 1.16 GB
US Congress votes 2004 67,392 3.61 MB
YAGO 44,000,000 4.0 GB
wikipedia 47,000,000 6.9 GB
(a) Dataset Cardinality
Benchmark Dataset # Queries # Basic Patterns (min;max)
1 US Congress votes 2004 9 (3;7)
2 US Congress votes 2004 20 (12;35)
3 YAGO 10 (17;25)
4 YAGO 9 (3;7)
5 YAGO 9 (17;27)
6 LUBM 8 (1,6)
(b) Query Benchmark Description
Fig. 1. Datasets and Benchmarks
Datasets: we used four different datasets: the Lehigh University Benchmark (LUBM) [7],
Wikipedia 8 , the real-world dataset on US Congress vote results of 2004, and the
7
Our extension of RDF-3X is able to efficiently execute bushy plans.
8
http://labs.systemone.net/wikipedia3
real-world ontology YAGO 9 . We generated the LUBM datasets for 1, 5, 10, 20
and 50 universities. The number of triples and size of the N-triple representation of
each dataset is described in Figure 1(a).
Query Benchmarks: We considered six sets of queries (Figure 1). Benchmark 1 is
comprised of queries that have at least one pattern whose object is instantiated
with a constant and the answer size varies from 3 to 14,868 triples. Benchmark 2
has queries composed between one and seven gjoin(s) among small-sized groups of
patterns; queries have more than 12 triple patterns. All queries in Benchmark 3 have
an empty answer, except q6 that produces 551 triples. Benchmark 4 is comprised
of queries that answer between 1 and 5,683 triples, while answers of Benchmark
5 queries range from 238 to 22,434. Finally, benchmark 6 is comprised of the first
eight queries of the LUBM benchmark. The first five benchmarks are published in
http://www.ldc.usb.ve/˜mvidal/OneQL/datasets/queries/.
Evaluation Metrics: We report on runtime performance and optimization time, which
are both measured by using the real time produced by the time command of the
Linux operation system. Runtime represents the elapsed time between the submis-
sion of the query and the output of the answer; optimization time just considers
the time elapsed between the submission of the query and the output of the query
physical plan. Experiments were run on a Sun Fire X4100 M2 machine with two
AMD Opteron 2000 Series processors, 1MB of cache per core and 8GB RAM,
running a 64-bit Linux CentOS 5.5 kernel. Queries in benchmark 4 and 5 were
run in cold-cache and warm cache. To run cold cache, we cleared the cache be-
fore running each query by performing the command sh -c "sync ; echo 3
> /proc/sys/vm/drop caches". To run on warm cache, we executed the same
query five times by dropping the cache just before running the first iteration of
the query. Additionally, the machine was dedicated exclusively to run these exper-
iments.
4.1 Loading and Indexing RDF Data
We used the LUBM dataset to study the time and memory consumed by RDF-3X,
AllegroGraph RDFStore, Jena, and Jena TDB during loading and indexing RDF data.
We report on the average execution time and the amount of main-memory required to
store the structures.
Figure 2(a) reports on the time (seconds and logarithm scale) required to load and
index all triples of each dataset. In all test cases, LUBM datasets allow us to observe
that RDF-3X is faster than the other RDF engines in loading and indexing RDF data.
This may be because RDF-3X exploits in-memory caching to maintain hash tables that
store: mappings between strings/URIs in RDF data to unsigned integer IDs, a single
triples table of these IDs, and the highly compressed indices of the triples table.
Figure 2(b) reports on the amount of main-memory required for the structures to
store all triples of a dataset (MB and logarithm scale). RDF-3X makes use of LZ77
compression in conjunction with byte-wise Gamma encoding for all the IDs and gap
compression [19]; thus, RDF-3X is able to consume less main-memory than the rest of
the RDF engines.
9
Ontology available for download at http://www.mpi-inf.mpg.de/yago-naga/yago/
Load and Index Time
10000.000
Time (secs. logarithmic Scale)
RDF-3X
1000.000 AllegroGraph
Jena
Jena TDB
100.000
10.000
1.000
Univ(1, 0) Univ(5, 0) Univ(10, 0) Univ(20, 0) Univ(50, 0)
Datasets
(a) Load and Index time-LUBM datasets (secs. and log scale)
Memory Size
10000.000
Memory Size (MB logarithmic
RDF-3X
AllegroGraph
1000.000
Jena
Jena TDB
scale)
100.000
10.000
1.000
Univ(1, 0) Univ(5, 0) Univ(10, 0) Univ(20, 0) Univ(50, 0)
Datasets
(b) Memory Size of Index Structures-LUBM datasets (MB and
log scale)
Fig. 2. LUBM datasets (logarithmic scale)
4.2 Query Optimization and Execution Techniques
We used all the datasets and benchmarks to analyze the RDF engines’ performance.
First, we studied how the shape of a query affects the optimization and execution time.
We considered the queries in benchmark 2, and for each query we built a bushy plan
comprised of star-shaped groups free of Cartesian products. We ran different versions
of Jena and Jena TDB, and we could observe that bushy plans comprised of star-shaped
groups outperform flat queries (Figure 3(a)).
Second, we analyzed how the size of a dataset can affect the performance of RDF
engines, i.e., we evaluated scalability. We used benchmark 6 against the LUBM datasets
for 1, 5, 10, 20, and 50 universities and studied the performance of these queries on
RDF-3X, AllegroGraph, Jena and Jena TDB. Figure 3(b) reports on the average query
execution time over each LUBM dataset (seconds and logarithmic scale). All the RDF
engines except Jena TDB, spent, on average, less than 1 second running the queries
against the dataset for 1 university. Also, all the queries were comprised of a large num-
ber of join binding patterns, thus the performance of the RDF-3X Merge join and in-
dices could be exploited and RDF-3X overcame other RDF engines in the five datasets.
In the case of Jena TDB, we turned off the optimizer and we could observe that queries
against the dataset with 50 universities timed out after 14 hours in Jena TDB.
Star-Shaped Group Plans Jena
Govtrack.us
10000
Star-shaped Plan
Original
1000
100
10
1
q3 q9 q10 q11 q12 q13 q14 q15 q16
Queries
(a) Effects Query Shape Jena (secs. and log scale)
Query Execution Time
100.000
RDF-3X
AllegroGraph
Time (secs-logarithmic scale)
Jena
Jena TDB
10.000
1.000
Univ(1, 0) Univ(5, 0) Univ(10, 0) Univ(20, 0) Univ(50, 0)
0.100
Datasets
(b) Average Query Execution Time-Benchmark 6 (secs. and log.
scale)
Fig. 3. Effects of the Query Shape
Third, we studied how the shape of the queries affects the cache usage by using
benchmarks 3, 4 and 5; for each query, we also built a bushy plan comprised of star-
shaped groups. We observed that in benchmark 3, RDF-3X is able to improve cold
cache execution times by a factor of 35 in the geometric mean when the queries were
run in warm cache. However, for benchmarks 4 and 5, we could observe that RDF-
3X performs poorly in warm cache and flat queries, while in bushy plans comprised
of star-shaped groups, CPU time was reduced from 96% to 25% and the cold cache
execution time was sped by up to one order of magnitude. Table 2 reports on cold cache
execution times, the minimum value observed during the execution in warm cache,
and the geometric means. Flat queries were run in RDF-3X, and the star-shaped group
bushy plans were run in GRDF-3X. First, flat queries are dominated by CPU-intensive
processing that consumed up to 98% of the CPU time; however, star-shaped group
bushy plans consumed up to 25% of the CPU time, and the execution time in both
cold and warm caches was reduced by up to five orders of magnitude. Finally, because
the star-shaped group queries were bushy trees comprised of small-sized star-shaped
groups, the number of intermediate results was smaller; thus, intermediate results could
be maintained in resident memory and used in further iterations.
Table 2. Benchmark 5-Run-Time Cold and Warm Caches (secs)
Cold Caches
Flat q1 q2 q3 q4 q5 q6 q7 q8 q9 Geom. Mean
Queries 62.30 84.87 100,657.34 85.95 61.2 188,909.69 0.14 1.47 827.75 166.03
Bushy
Trees 1.60 1.80 2.34 1.22 1.38 1.36 0.99 1.05 1.75 1.45
Warm Caches
Flat q1 q2 q3 q4 q5 q6 q7 q8 q9 Geom. Mean
Queries 58.21 59.54 72,584.71 58.52 59.73 175,909.80 0.14 1.46 808.77 144.13
Bushy
Trees 0.34 0.26 0.93 0.14 0.31 0.17 0.12 0.31 0.69 0.29
Finally, we used benchmarks 4 and 5 to study how query plan shapes affect the
performance of the RDF-3X optimizer. We could observe that for some queries, more
than 93% of the total execution time was spent in query optimization and the gener-
ation of the physical plan. The reason for this is that the RDF-3X optimizer relies on
a dynamic-based programming algorithm that traverses the space of linear plans in it-
erations, where linear sub-plans comprised of i joins are generated during iteration i.
Although the space of linear plans can be considerably smaller than the space of bushy
plans, this approach does not scale up to queries of more than 20 triple patterns with
star-shaped join binding patterns, such as the ones in benchmark 5.
5 Conclusions
In this paper, we characterized some of the properties of the benchmarks that can be
used to evaluate the performance of existing RDF engines when RDF structures are
created and indexed, and queries are optimized and executed. We have identified that
dataset sizes and query plan shapes must also be considered during benchmarking to
exhibit the performance of existing RDF engines.
First, we have confirmed that the RDF-3X query engine and data structures scale up
to large datasets and to large number of chained join binding patterns. We also reported
experimental results suggesting that the benefits of running in warm caches depend on
the shape of executed queries. For simple queries, the RDF-3X engine is certainly able
to benefit from warming up cache; however, for queries with several star-shaped groups,
the RDF-3X optimizer generates left-linear plans that may produce a large number of
intermediate results or require CPU-intensive processing that degrades the RDF-3X
engine performance in both cold and warm caches. On the other hand, if these queries
are rewritten as bushy plans, the number of intermediate results and the CPU processing
can be reduced and the performance improves. So, we recommend to consider the query
plan shape during benchmarking.
In the future we plan to study the effects of the correlations between the instantia-
tions of the queries and between the subjects, predicates and objects of the RDF data;
additionally, we plan to conduct a similar study in Sesame, MonetDB, BitMat and RD-
FVector. Finally, developing tools able to generate the described benchmarks, is also in
our future plans.
References
1. D. J. Abadi, A. Marcus, S. Madden, and K. Hollenbach. SW-Store: a vertically partitioned
DBMS for Semantic Web data management. VLDB Journal, 18(2):385–406, 2009.
2. D. J. Abadi, A. Marcus, S. Madden, and K. J. Hollenbach. Scalable Semantic Web Data
Management Using Vertical Partitioning. In Proceedings of the International Conference on
Very Large Data Bases (VLDB), pages 411–422, 2007.
3. AllegroGraph. http://www.franz.com/agraph/allegrograph/, 2009.
4. M. Atre, V. Chaoji, M. J. Zaki, and J. A. Hendler. Matrix ”Bit” loaded: a scalable lightweight
join query processor for RDF data. In Proceedings of the WWW, pages 41–50, 2010.
5. C. Bizer and A. Schultz. The berlin sparql benchmark. Int. J. Semantic Web Inf. Syst.,
5(2):1–24, 2009.
6. G. Fletcher and P. Beck. Scalable Indexing of RDF Graph for Efficient Join Processing. In
CIKM, 2009.
7. Y. Guo, Z. Pan, and J. Heflin. An Evaluation of Knowledge Base Systems for Large OWL
Datasets. In Proceedings of the International Semantic Web Conference (ISWC), pages 274–
288, Japan, 2004.
8. Y. Guo, Z. Pan, and J. Heflin. Lubm: A benchmark for owl knowledge base systems. J. Web
Sem., 3(2-3):158–182, 2005.
9. Y. Guo, A. Qasem, Z. Pan, and J. Heflin. A requirements driven framework for benchmarking
semantic web knowledge base systems. IEEE Trans. Knowl. Data Eng., 19(2):297–309,
2007.
10. A. Harth, J. Umbrich, A. Hogan, and S. Decker. YARS2: A Federated Repository for Query-
ing Graph Structured Data from the Web. In Proceedings of the The Semantic Web, 6th In-
ternational Semantic Web Conference, 2nd Asian Semantic Web Conference (ISWC/ASWC),
pages 211–224, 2007.
11. G. Ianni, T. Krennwallner, A. Martello, and A. Polleres. A Rule System for Querying
Persistent RDFS Data. In Proceedings of the 6th European Semantic Web Conference
(ESWC2009), Heraklion, Greece, May 2009. Springer. Demo Paper.
12. S. Idreos, M. L. Kersten, and S. Manegold. Self-organizing tuple reconstruction in column-
stores. In Proceedings of the ACM SIGMOD International Conference on Management of
Data, pages 297–308, 2009.
13. Jena Ontology Api. http://jena.sourceforge.net/ontology/index.html, 2009.
14. Jena TDB. http://jena.hpl.hp.com/wiki/TDB, 2009.
15. T. Lampo, E. Ruckhaus, J. Sierra, M.-E. Vidal, and A. Martinez. OneQL: An Ontology-based
Architecture to Efficiently Query Resources on the Semantic Web. In The 5th International
Workshop on Scalable Semantic Web Knowledge Base Systems at the International Semantic
Web Conference (ISWC), 2009.
16. J. McGlothlin. RDFVector: An Efficient and Scalable Schema for Semantic Web Knowledge
Bases. In Proceedings of the PhD Symposium ESWC, 2010.
17. J. McGlothlin and L. Khan. RDFJoin: A Scalable of Data Model for Persistence and Efficient
Querying of RDF Dataasets. In Proceedings of the International Conference on Very Large
Data Bases (VLDB), 2009.
18. T. Neumann and G. Weikum. RDF-3X: a RISC-style engine for RDF. PVLDB, 1(1):647–
659, 2008.
19. T. Neumann and G. Weikum. Scalable join processing on very large rdf graphs. In Pro-
ceedings of the ACM SIGMOD International Conference on Management of Data, pages
627–640, 2009.
20. M. Schmidt, T. Hornung, N. Küchlin, G. Lausen, and C. Pinkel. An experimental comparison
of rdf data management approaches in a sparql benchmark scenario. In Proceedings of the
International Semantic Web Conference (ISWC), pages 82–97, 2008.
21. L. Sidirourgos, R. Goncalves, M. L. Kersten, N. Nes, and S. Manegold. Column-store sup-
port for RDF data management: not all swans are white. PVLDB, 1(2):1553–1563, 2008.
22. M.-E. Vidal, E. Ruckhaus, T. Lampo, A. Martinez, J. Sierra, and A. Polleres. Efficiently
Joining Group Patterns in SPARQL Queries. In Proceedings of the 7th Extended Semantic
Web Conference (ESWC2010), 2010.
23. C. Weiss and A. Bernstein. On-disk storage techniques for semantic web data are b-trees
always the optimal solution? In The 5th International Workshop on Scalable Semantic Web
Knowledge Base Systems at the International Semantic Web Conference (ISWC), 2009.
24. C. Weiss, P. Karras, and A. Bernstein. Hexastore: sextuple indexing for semantic web data
management. PVLDB, 1(1):1008–1019, 2008.
25. J. Wielemaker. An Optimised Semantic Web Query Language Implementation in Prolog. In
Proceedings of the ICLP Conference, pages 128–142, 2005.
26. K. Wilkinson, C. Sayers, H. Kuno, D. Reynolds, and J. Database. Efficient RDF Storage and
Retrieval in Jena2. In EXPLOITING HYPERLINKS 349, pages 35–43, 2003.