Abstract
While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Today’s multicore computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amdahl, G.: Validity of the single processor approach to achieving large-scale computing capabilities. AFIPS Conference Proceedings (30), pp. 483–485 (1967)
Boral, H., Alexander, W., Clay, L., Copeland, G., Danforth, S., Franklin, M., Hart, B., Smith, M., Valduriez, P.: Prototyping Budda: a highly parallel database system. IEEE Knowledge and Data Engineering, vol. 2, no. 1, March (1990)
DeWitt, D., Gray, J.: Parallel database systems: the future of high performance database systems. Communications of ACM 35(6), 85–98 (1992)
DeWitt, D.J., Gerber, R.H., Graefe, G., Heytens, M.L., Kumar, K.B.: GAMMA – A high performance dataflow database machine. VLDB, 1986
Graefe, G.: Query evaluation techniques for large database. ACM Computing Surveys 25, 2 (June), 73–170 (1993)
Groppe, J., Groppe, S.: Parallelizing join computations of SPARQL queries for large semantic web databases. In: 26th symposium on applied computing (ACM SAC 2011), TaiChung, Taiwan (2011)
Groppe, J., Groppe, S., Schleifer, A.: Visual query system for analyzing social semantic Web. In: 20th International World Wide Web Conference (WWW 2011), Hyderabad, India (2011)
Groppe, S., Groppe, J., Klein, N., Bettentrupp, R., Böttcher, S., Gruenwald, L.: Transforming XSLT stylesheets into XQuery expressions and vice versa. Computer Languages, Systems and Structures Journal 37(3), 76–111 (2011c)
Hoare, C.A.R.: Monitors: an operating system structuring concept. Commun. ACM 17(10), 549–557 (1974)
Kitsuregawa, M., Tanaka, H., Moto-oka, T.: Application of hash to data base machine and its architecture. New Generation Computing 1(1) (1983)
Kitsuregawa, M., Ogawa, Y.: A new parallel Hash join method with robustness for data skew in super database computer (SDC), In: Proceedings of the Sixteenth International Conference on Very Large Data Bases. Melbourne, Australia, August (1990)
Mishra, P., Eich, M.: Join processing in relational databases. ACM Computing Surveys 24(1), 63–113 (1992)
Neumann, T., Weikum, G.: Scalable join processing on very large RDF graphs, SIGMOD (2009)
Neumann, T., Weikum, G.: RDF3X: a RISCstyle engine for RDF. In: Proceedings of the 34th International Conference on Very Large Data Bases (VLDB). Auckland, New Zealand (2008)
Piatetsky-Shapiro, G., Connell, C.: Accurate estimation of the number of tuples satisfying a condition. SIGMOD, (1984)
Schneider, D., DeWitt D.: A performance evaluation of four parallel join algorithms in a shared-nothing multiprocessor environment, In: Proceedings of the 1989 SIGMOD Conference. Portland, OR, June 1989
Schneider, D., DeWitt, D.: Tradeoffs in processing complex join queries via Hashing in multiprocessor database machines. In: Proceedings of the Sixteenth International Conference on Very Large Data Bases, Melbourne, Australia, August (1990)
Weiss, C., Karras, P., Bernstein, A.: Hexastore: sextuple indexing for semantic web data management. VLDB (2008)
Wolf, J.L., Dias, D.M., Yu P.S.: An effective algorithm for parallelizing sort-merge joins in the presence of data Skew. In: 2nd International Symposium on Databases in Parallel and Distributed Systems. (1990)
Zeller, H.J., Gray, J.: Adaptive hash joins for a Multiprogramming Environment. In: Proceedings of the 1990 VLDB Conference. Australia, August 1990
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Groppe, S. (2011). Parallel Databases. In: Data Management and Query Processing in Semantic Web Databases. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19357-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-19357-6_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19356-9
Online ISBN: 978-3-642-19357-6
eBook Packages: Computer ScienceComputer Science (R0)