"LargeRDFBench: A billion triples benchmark for SPARQL endpoint federation" as presented in the 17th International Semantic Web Conference ISWC ( ournal track), 8th - 12th of October 2018, held in Monterey, California, USA This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).

1. Muhammad Saleem , Ali Hasnai, Axel-Cyrille Ngonga Ngomo
AKSW, University of Leipzig, Germany
DICE, University of Paderborn, Germany
INSIGHT, University of Galway,Ireland
1
(ISWC 2018, USA)

2.  Federated Benchmark Design Features
 Why LargeRDFBench?
 Evaluation and results
2

3.  Datasets
 Queries
 Performance metrics
 Execution rules
3

4. Datasets used in the federation benchmark should vary:
 Number of triples
 Number of classes
 Number of resources
 Number of properties
 Number of objects
 Average properties per class
 Average instances per class
 Average in-degree and out-degree
 Structuredness or coherence
4

5.  Number of triple patterns
 Number of join vertices
 Mean join vertex degree,
 Number of sources span
 Query result set sizes
 Mean triple pattern selectivity
 BGP-restricted triple pattern selectivity
 Join-restricted triple pattern selectivity
 Join vertex types (`star', `path', `hybrid', `sink')
 SPARQL clauses used (e.g., LIMIT, UNION, OPTIONAL, FILTER etc.)
5

6.  Result set completeness and correctness
 Number of sources selected
 Number of SPARQL ASK requests used during source selection
 Source selection time
 Number of endpoint requests
 Number of intermediate results
 Overall query execution time
6

7.  SPARQL query federation benchmark
 13 interconnect real datasets
 4 Life sciences
 6 Cross domain
 3 Large data
 40 queries of varying complexities
 14 simple (from FedBench)
 10 complex
 8 large data
 8 complex plus high data sources
 Multiple performance metrics
7

8. 8
Why LargeRDFBench?

9. 9
0
0,2
0,4
0,6
0,8
1
1,2
Structuredness
FedBench FedBench-Mean LargeRDFBench LargeRDFBench-Mean
 FedBench
 Min. = 0.19
 Max. = 0.91
 STD. = ± 0.26
 LargeRDFBench
 Min. = 0.19
 Max. = 1
 STD. = ± 0.28

10. 10
1,E+00
1,E+01
1,E+02
1,E+03
1,E+04
1,E+05
1,E+06
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
L1
L2
L3
L4
L5
L6
L7
L8
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
#Results(logscale)
FedBench
FedBench-Mean
LargeRDFBench
LargeRDFBench-Mean
 FedBench
 Min. = 1
 Max. = 9054
 STD. = ± 2397
 LargeRDFBench
 Min. = 1
 Max. = 306705
 STD. = ± 104236

11. 11
 FedBench
 FedX = 7.4 sec
 SPLENDID = 53.4 sec
 ANAPSID = 12.4 sec
 SemaGrow = 12 sec
 CostFed = 0.44 sec
 LargeRDFBench (complex queries)
 FedX = 246 sec
 SPLENDID = 212 sec
 ANAPSID = 147 sec
 SemaGrow = 367 sec
 CostFed = 122 sec
 LargeRDFBench (LargeData queries)
 Greater than 1 hour for all engines

12. 12
0
5
10
15
20
25
30
35
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
L1
L2
L3
L4
L5
L6
L7
L8
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
#TriplePatterns
FedBench
FedBench-Mean
LargeRDFBench
LargeRDFBench-Mean
 FedBench
 Min. = 2
 Max. = 7
 STD. = ± 1.33
 LargeRDFBench
 Min. = 2
 Max. = 33
 STD. = ± 6.15

13. 13
0
2
4
6
8
10
12
14
16
18
20
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
L1
L2
L3
L4
L5
L6
L7
L8
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
#JoinVertices
FedBench
FedBench-Mean
LargeRDFBench
LargeRDFBench-Mean
 FedBench
 Min. = 0
 Max. = 5
 STD. = ± 1.33
 LargeRDFBench
 Min. = 0
 Max. = 19
 STD. = ± 3.63

14. 14
0
1
2
3
4
5
6
7
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
L1
L2
L3
L4
L5
L6
L7
L8
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
MeanJoinVerticesDegree
FedBench
FedBench-Mean
LargeRDFBench
LargeRDFBench-Mean
 FedBench
 Min. = 2
 Max. = 3
 STD. = ± 0.3
 LargeRDFBench
 Min. = 2
 Max. = 6
 STD. = ± 0.72

15. 15
0
2
4
6
8
10
12
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
L1
L2
L3
L4
L5
L6
L7
L8
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
#RelevantSources
FedBench
FedBench-Mean
LargeRDFBench
LargeRDFBench-Mean
 FedBench
 Min. = 1
 Max. = 4
 STD. = ± 0.66
 LargeRDFBench
 Min. = 1
 Max. = 10
 STD. = ± 2.1

16. 16
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
L1
L2
L3
L4
L5
L6
L7
L8
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
MeanTriplePatternSelectivity
FedBench LargeRDFBench
 FedBench
 Min. = 0.0011
 Max. = 0.3335
 STD. = ± 0.11
 LargeRDFBench
 Min. = 0.00004
 Max. = 0.4858
 STD. = ± 0.13

17. 17
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
L1
L2
L3
L4
L5
L6
L7
L8
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
MeanBGP-restrictedTriplePattern
Selectivity
FedBench LargeRDFBench
 FedBench
 Min. = 0.0003
 Max. = 1
 STD. = ± 0.31
 LargeRDFBench
 Min. = 0.0003
 Max. = 1
 STD. = ± 0.22

18. 18
0,000001
0,00001
0,0001
0,001
0,01
0,1
1
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
L1
L2
L3
L4
L5
L6
L7
L8
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
MeanJoin-restrictedTriplePattern
Selectivity(logscale)
FedBench LargeRDFBench
 FedBench
 Min. = 0.0
 Max. = 0.33
 STD. = ± 0.13
 LargeRDFBench
 Min. = 0.0
 Max. = 0.58
 STD. = ± 0.15

19. 19
1,E+00
1,E+01
1,E+02
1,E+03
1,E+04
1,E+05
1,E+06
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Avg.
Time-LogScale(msec)
FedX(cold) FedX(100% cached) SPLENDID ANAPSID FedX+HiBISCuS SPLENDID+HiBISCuS
FedX+HiBISCuS FedX SPLENDID+HiBISCuS ANAPSID SPLENDID

20. 20
SPLENDID+HiBISCuS SPLENDID FedX+HiBISCuS FedX
1,E+00
1,E+01
1,E+02
1,E+03
1,E+04
1,E+05
1,E+06
1,E+07
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 Avg.
Time-LogScale(msec)
FedX(cold) FedX(100% cached) SPLENDID ANAPSID FedX+HiBISCuS SPLENDID+HiBISCuS
Runtimeerror
Runtimeerror
Runtimeerror
Runtimeerror
Runtimeerror
Runtimeerror
Timeout
Runtimeerror
Timeout
Timeout
Timeout
Timeout
Timeout
Timeout
Timeout
Runtimeerror
ANAPSID

21. 21
Query FedX(100% cached) SPLENDID ANAPSID FedX+HiBISCuS SPLENDID+HiBISCuS
L1 2320 (7.2 %) 16 (2.73 %) 1947 (15.76 %) 2320 (7.2 %) 16 (2.73 %)
L2 1 (0 %) 80 (1.8 %) 1609 (ZRT) 1 (0 %) 80 (1.80 %)
L3 1 (0 %) 27345725553 (ZRT) 1 (0 %) 2734572
L4 3967 (0.08 %) 16321 (0 %) 11290 (0 %) 15721 (48.34 %) 16220 (0 %)
L5 1 (0 %) 28342 (ZRE) 3840 (ZRT) 1 (0 %) 28212 (ZRE)
L6 3830809 (ZRT) 61810 (ZRE) 1707 (ZRT) 3414400 (0 %) 61419 (ZRE)
L7 74387 867628 267 23381 1341384
L8 206859 (0.01 %) 2423783 (0.05 %) 17302 (0.05 %) 206859 (0.01 %) 2423783 (0.05 %)
ZRT = Zero Results after Timeout
ZRE = Zero Results with Runtime Error

22. 22
Query CostFed FedX SPLENDID ANAPSID FedX+HiBISCuS SPLENDID+HiBISCuS SemaGrow
CH1 800 6321Runtime Error Timeout 4109Runtime Error 277435
CH2 Runtime Error 3282233Runtime Error 101517 778563Runtime Error 144326
CH3 Zero Result 275412Runtime Error Zero Results 329223Runtime Error 4660
CH4 Runtime Error Zero Results Runtime Error 27544Zero Results Runtime Error 10606
CH5 52875Timeout 15122Parse Error Runtime Error 15122Timeout
CH6 173 274Runtime Error 7737Runtime Error Runtime Error Timeout
CH7 5647Timeout Runtime Error 2480Runtime Error Runtime Error Timeout
CH8 Timeout Timeout Runtime Error Timeout Runtime Error Runtime Error Timeout
Most engines are somehow unstable

23. 23
FedX(Warm) SPLENDID ANAPSID FedX+HiBISCuS SPLENDID+HiBISCuS
#Triple Patterns 0.537 0.453 0.621 0.492
#Sources Span 0.233 0.232 0.245 0.019 0.290
#Results 0.583 0.553 0.085 0.534 0.476
#Join Vertices 0.275 0.289 0.214 0.301 0.284
Mean Join Vertex Degree 0.500 0.210 0.226 0.382 0.183
Mean TP Selectivity 0.261 0.304 0.198 0.237 0.263
Mean BGP-restricted TP Sel -0.065 -0.022 -0.190 -0.014 -0.042
Mean Join-restricted TP Sel 0.654 -0.334 -0.224 -0.472 -0.441
 Results are significant at 1% level
 Results are significant at 5% level
 Results are significant at 10% level
Most influential Features
# Triple Pattern Result size Join-restricted TP selectivity
Number of join vertices Mean Join vertex degree Mean TP
selectivity Number of sources span BGP-restricted TP
selectivity

24.  Simple queries benchmarks are not sufficient to perform a fair
comparison of federation engines
 Positioning of federation engines greatly changes from Simple to
Complex queries
 Federation engines are unstable when exposed to Large Data or
Complex + High Sources queries
 Number of triple patterns, Result Size, and Join-restricted TP
selectivity are the three most influential query features
 Smaller number of endpoints requests does not necessary mean
smaller execution time
24

25. This work was supported by grants from the EU H2020 Framework Programme
provided for the project HOBBIT (GA no. 688227).

26. Thanks !
saleem@informatik.uni-leipzig.de
26

27. 27

28. SPARQL ENDPOINTS QUERY FEDERATION
28
Endpoint 1 Endpoint 2 Endpoint 3 Endpoint 4
RDF RDF RDF RDF
Parsing
Source Selection
Federator Optimzer
Integrator
Rewrite query and
get Individual
Triple Patterns
Identify capable
source against
Individual Triple
Patterns
Generate
optimized sub-
query Exe. Plan
Integrate sub-
queries results
Execute sub-
queries
Federation
Engine

29. QUERIES AS HYPERGRAPHS
29