Q. :. Ub, S ub:memberOf ?D . ?D ub:subOrganizationOf <University0> } Q4(original): SELECT ?X ?Y WHERE { ?X rdf:type ub:Lecturer . ?Y rdf:type ub:Department . ?X ub:worksFor ?Y . ?Y ub:subOrganizationOf <University0> } Q5: SELECT ?X ?Y ?Z WHERE { ?X rdf:type ub:UndergraduateStudent . ?Y rdf:type ub:FullProfessor . ?Z rdf:type ub:Course . ?X ub rdf:type ub:UndergraduateStudent . ?Y rdf:type ub:FullProfessor . ?Z rdf:type ub:Course . ?X ub:advisor ?Y . ?Y ub:teacherOf ?Z } Q7: SELECT ?X ?Y ?Z WHERE { ?X a ub:GraduateStudent . ?Z ub:subOrganizationOf ?Y . ?X ub:memberOf ?Z . ?Z a ub:Department . ?Y a ub:University . } Q8: SELECT ?X ?Y ?Z WHERE { ?X a ub:GraduateStudent . ?X ub:undergraduateDegreeFrom ?Y. ?Z ub:subOrganizationOf ?Y . ?Z a ub:Department . ?Y a ub:University . } Q9(original): SELECT ?X ?Y ?Z WHERE { ?X a ub:GraduateStudent . ?X ub:undergraduateDegreeFrom ?Y. ?Z ub:subOrganizationOf ?Y . ?X ub:memberOf ?Z . ?Z a ub:Department . ?Y a ub:University . } Q10(original): SELECT ?X ?Y ?Z WHERE { ?X rdf:type ub:Undergraduate Student . ?Y rdf:type ub:FullProfessor . ?Z rdf:type ub:Course . ?X ub:advisor ?Y . ?X ub:takesCourse ?Z . ?Y ub:teacherOf ?Z } Q11: SELECT ?X ?Y ?E WHERE { ?X rdf:type ub:Undergraduate Student . ?X ub:takesCourse ?Y . ?X ub:memberOf ?Z . ?X ub:advisor ?W . ?W rdf:type ub:FullProfessor . ?W ub:emailAddress ?E . ?Z ub:subOrganizationOf ?U . ?U ub:name " University3 " } Q12: SELECT ?X ?Y ?Z WHERE { ?X rdf:type ub:FullProfessor . ?X ub:teacherOf ?Y . ?Y rdf:type ub:GraduateCourse . ?X ub:worksFor ?Z . ?W ub:advisor ?X . ?W rdf:type ub:GraduateStudent . ?W ub:emailAddress ?E . ?Z rdf:type ub:Department . ?Z ub:subOrganizationOf ?U } Q13: SELECT ?X ?Y ?Z WHERE ?X rdf:type ub:FullProfessor . ?X ub:teacherOf ?Y . ?Y rdf:type ub:GraduateCourse . ?X ub:worksFor ?Z . ?W ub:advisor ?X, rdf:type ub:GraduateStudent . ?W ub:emailAddress ?E . ?Z rdf:type ub:Department . ?Z ub:subOrganizationOf <University0> Q14: SELECT ?X ?Y ?Z WHERE { ?X rdf:type ub:FullProfessor . ?X ub:teacherOf ?Y . ?Y rdf:type ub:GraduateCourse . ?X ub:worksFor ?Z . ?W ub:advisor ?X . ?W rdf:type ub:GraduateStudent . ?W References [1] Daniel J. Abadi, Adam Marcus, and Barton Data. Scalable Semantic Web Data Management using Vertical Partitioning VLDB, 2007.

N. Foto, J. D. Afrati, and . Ullman, Optimizing Joins in a Map-Reduce Environment, EDBT, 2010.

S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak et al., DBpedia: A Nucleus for a Web of Open Data, ISWC, 2007.
DOI : 10.1007/978-3-540-76298-0_52

S. Blanas, J. M. Patel, E. J. Vuk-ercegovac-rao, Y. Shekita, and . Tian, A comparison of join algorithms for log processing in MaPreduce, Proceedings of the 2010 international conference on Management of data, SIGMOD '10, 2010.
DOI : 10.1145/1807167.1807273

M. Cai, M. R. Frank, B. Yan, and R. M. Macgregor, A subscribable peer-to-peer RDF repository for distributed metadata management, Web Semantics: Science, Services and Agents on the World Wide Web, vol.2, issue.2, pp.109-130, 2004.
DOI : 10.1016/j.websem.2004.10.003

E. Chong, S. Das, G. Eadon, and J. Srinivasan, An Efficient SQL-based RDF Querying Scheme, VLDB, 2005.

M. Y. Eltabakh, Y. Tian, F. Özcan, R. Gemulla, A. Krettek et al., CoHadoop, Proceedings of the VLDB Endowment, vol.4, issue.9, 2011.
DOI : 10.14778/2002938.2002943

L. Galarraga, K. Hose, and R. Schenkel, Partout, Proceedings of the 23rd International Conference on World Wide Web, WWW '14 Companion, 2012.
DOI : 10.1145/2567948.2577302

A. Gates, J. Dai, and T. Nair, Apache Pig's optimizer, IEEE Data Eng. Bull, vol.36, issue.1, p.2013

F. Goasdoué, K. Karanasos, J. Leblay, and I. Manolescu, View selection in Semantic Web databases, Proceedings of the VLDB Endowment, vol.5, issue.2, p.2012
DOI : 10.14778/2078324.2078326

A. Gubichev and T. Neumann, Exploiting the query structure for efficient join ordering in SPARQL queries, EDBT, 2014.

Y. Guo, Z. Pan, and J. Heflin, LUBM: A benchmark for OWL knowledge base systems, Web Semantics: Science, Services and Agents on the World Wide Web, vol.3, issue.2-3, 2005.
DOI : 10.1016/j.websem.2005.06.005

S. Gurajada, S. Seufert, I. Miliaraki, and M. Theobald, TriAD, Proceedings of the 2014 ACM SIGMOD international conference on Management of data, SIGMOD '14, 2014.
DOI : 10.1145/2588555.2610511

H. Herodotou, F. Dong, and S. Babu, MapReduce Programming and Cost-based Optimization? Crossing this Chasm with Starfish, PVLDB, vol.4, issue.12, 2011.

K. Hose and R. Schenkel, WARP: Workload-aware replication and partitioning for RDF, 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), 2013.
DOI : 10.1109/ICDEW.2013.6547414

M. Husain, J. Mcglothlin, M. M. Masud, L. Khan, and B. M. Thuraisingham, Heuristics-Based Query Processing for Large RDF Graphs Using Cloud Computing, IEEE Transactions on Knowledge and Data Engineering, vol.23, issue.9, 2011.
DOI : 10.1109/TKDE.2011.103

D. Huynh, S. Mazzocchi, and D. R. Karger, Piggy Bank: Experience the Semantic Web inside your web browser, J. Web Sem, vol.5, issue.1, 2007.

D. Jiang, B. C. Ooi, L. Shi, and S. Wu, The performance of MapReduce, Proceedings of the VLDB Endowment, vol.3, issue.1-2, 2010.
DOI : 10.14778/1920841.1920903

Z. Kaoudi and I. Manolescu, RDF in the clouds: a survey, The VLDB Journal, vol.1, issue.1, 2014.
DOI : 10.1007/s00778-014-0364-z
URL : https://hal.archives-ouvertes.fr/hal-01020977

R. Karp, Reducibility among combinatorial problems, Complexity of Computer Computations, pp.85-103, 1972.

K. Lee and L. Liu, Scaling queries over big RDF graphs with semantic hash partitioning, Proceedings of the VLDB Endowment, vol.6, issue.14, 2013.
DOI : 10.14778/2556549.2556571
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.401.3362

F. Li, B. C. Ooi, M. T. Özsu, and S. Wu, Distributed data management using MapReduce, ACM Computing Surveys, vol.46, issue.3, p.31, 2014.
DOI : 10.1145/2503009
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.387.7399

T. Neumann and G. Weikum, The RDF-3X engine for scalable management of RDF data, The VLDB Journal, vol.29, issue.3, 2010.
DOI : 10.1007/s00778-009-0165-y

M. T. Özsu and P. Valduriez, Distributed and Parallel Database Systems (3rd, 2011.

N. Papailiou, I. Konstantinou, and D. Tsoumakos, Panagiotis Karras, and Nectarios Koziris. H2RDF+: High-performance Distributed Joins over Large-scale RDF Graphs, IEEE BigData, 2013.

E. Prud-'hommeaux and A. Seaborn, SPARQL Query Language for RDF, W3C Recommendation, 2008.

R. Ramakrishnan and J. Gehrke, Database Management Systems (3rd, 2003.

M. Fabian, G. Suchanek, G. Kasneci, and . Weikum, Yago: A Core of Semantic Knowledge, WWW, 2007.

A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka et al., Hive - a petabyte scale data warehouse using Hadoop, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), 2010.
DOI : 10.1109/ICDE.2010.5447738

P. Tsialiamanis, L. Sidirourgos, I. Fundulaki, V. Christophides, and P. A. Boncz, Heuristics-based query optimisation for SPARQL, Proceedings of the 15th International Conference on Extending Database Technology, EDBT '12, 2012.
DOI : 10.1145/2247596.2247635
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.366.1216

S. Wu, F. Li, S. Mehrotra, and B. C. Ooi, Query optimization for massively parallel data processing, Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC '11, 2011.
DOI : 10.1145/2038916.2038928

. Inria, Query graph model, 2010.

K. Zeng, J. Yang, H. Wang, B. Shao, and Z. Wang, A distributed graph engine for web scale RDF data, Proceedings of the VLDB Endowment, vol.6, issue.4, 2013.
DOI : 10.14778/2535570.2488333

X. Zhang, L. Chen, Y. Tong, and M. Wang, EAGRE: Towards Scalable I/O Efficient SPARQL Query Evaluation on the Cloud, ICDE, 2013.