Scalable RDF Data Compression using X10
The Semantic Web comprises enormous volumes 
of semi-structured data elements. For interoperability, these 
elements are represented by long strings. Such representations 
are not efficient for the purposes of Semantic Web applications 
that perform computations over large volumes of information. 
A typical method for alleviating the impact of this problem is 
through the use of compression methods that produce more 
compact representations of the data. The use of dictionary 
encoding for this purpose is particularly prevalent in Semantic 
Web database systems. However, centralized implementations 
present performance bottlenecks, giving rise to the need for 
scalable, efficient distributed encoding schemes. In this paper, 
we describe an encoding implementation based on the asynchronous 
partitioned global address space (APGAS) parallel 
programming model. We evaluate performance on a cluster of 
up to 384 cores and datasets of up to 11 billion triples (1.9 
TB). Compared to the state-of-art MapReduce algorithm, we 
demonstrate a speedup of 2:6 - 7:4X and excellent scalability. 
These results illustrate the strong potential of the APGAS 
model for efficient implementation of dictionary encoding and 
contributes to the engineering of larger scale Semantic Web 
applications.