Efficient RDF Representation and Parallel Join Processing Algorithm on General Purpose Many-Core

The more RDF data increase, the more difficulty we can get SPARQL query result fast when we use single processing. So it is necessary to adapt parallel processing in processing SPARQL query. This paper propose effective RDF structure and parallel SPARQL algorithm using GPU. In experiment, we evaluate our algorithm with previous work in terms of data size and query processing performance. Though experiment, we could know our system reduce averagely 50% data size and improve 20% performance in merging process.

[1]  Bhavani M. Thuraisingham,et al.  Data Intensive Query Processing for Large RDF Graphs Using Cloud Computing Tools , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[2]  Abraham Bernstein,et al.  Hexastore: sextuple indexing for semantic web data management , 2008, Proc. VLDB Endow..

[3]  Sangyoon Oh,et al.  Scalable RDF triple store using summary of hashed information and Bit comparison , 2015, 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM).

[4]  Haixun Wang,et al.  A Distributed Graph Engine for Web Scale RDF Data , 2013, Proc. VLDB Endow..

[5]  Ioannis Konstantinou,et al.  H2RDF: adaptive query processing on RDF data in the cloud. , 2012, WWW.

[6]  Andreas Harth,et al.  Optimized index structures for querying RDF from the Web , 2005, Third Latin American Web Congress (LA-WEB'2005).

[7]  Gerhard Weikum,et al.  RDF-3X: a RISC-style engine for RDF , 2008, Proc. VLDB Endow..

[8]  Yao Zhang,et al.  Scan primitives for GPU computing , 2007, GH '07.

[9]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[10]  Ioana Manolescu,et al.  RDF in the clouds: a survey , 2014, The VLDB Journal.

[11]  James A. Hendler,et al.  BitMat: A Main-memory Bit Matrix of RDF Triples for Conjunctive Triple Pattern Queries , 2008, SEMWEB.