Computer Science – Information Retrieval
Scientific paper
2011-05-20
Computer Science
Information Retrieval
In Proc. of AMCIS'2011
Scientific paper
Current "data deluge" has flooded the Web of Data with very large RDF datasets. They are hosted and queried through SPARQL endpoints which act as nodes of a semantic net built on the principles of the Linked Data project. Although this is a realistic philosophy for global data publishing, its query performance is diminished when the RDF engines (behind the endpoints) manage these huge datasets. Their indexes cannot be fully loaded in main memory, hence these systems need to perform slow disk accesses to solve SPARQL queries. This paper addresses this problem by a compact indexed RDF structure (called k2-triples) applying compact k2-tree structures to the well-known vertical-partitioning technique. It obtains an ultra-compressed representation of large RDF graphs and allows SPARQL queries to be full-in-memory performed without decompression. We show that k2-triples clearly outperforms state-of-the-art compressibility and traditional vertical-partitioning query resolution, remaining very competitive with multi-index solutions.
Álvarez-García Sandra
Brisaboa Nieves R.
Fernández Javier D.
Martínez-Prieto Miguel A.
No associations
LandOfFree
Compressed k2-Triples for Full-In-Memory RDF Engines does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Compressed k2-Triples for Full-In-Memory RDF Engines, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Compressed k2-Triples for Full-In-Memory RDF Engines will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-648746