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  1. Proceedings of the 5th ACM workshop on HotPlanet (HotPlanet '13)
  2. Efficient social network data query processing on MapReduce
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The 5th ACM hotplanet workshop keynote speaker: smartphones, crowds, and the cloud: population guided sensing systems
What's in a name?: decoding router interface names
Efficient social network data query processing on MapReduce
Lessons learned from the netsense smartphone study
Trace selection for improved WLAN monitoring
PIXS: programmable intelligence for cross-platform socialization
Information bazaar: a contextual evaluation
Internet atlas: a geographic database of the internet
Extrapolating sparse large-scale GPS traces for contact evaluation
It's tea time: do you know where your mug is?
A framework for monitoring and measuring a large-scale distributed system in real time
Metric convergence in social network sampling

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Efficient social network data query processing on MapReduce

Content Provider ACM Digital Library
Author Liu, Liu Gao, Lixin Yin, Jiangtao
Abstract Social network data analysis becomes increasingly important for business intelligence and online social services. Lots of social network data is presented by Resource Description Framework (RDF). Accordingly, SPARQL, an RDF query language, becomes popular for social network data analysis. As the sizes of social networks expand rapidly, a SPARQL query usually involves a large quantity of data, and thus parallelizing its execution is desirable. MapReduce is a well-known and popular big data analysis tool. However, the state-of-the-art translation from SPARQL queries to MapReduce jobs is not efficient because it mainly follows a two layer rule which needs to transform the SPARQL triple pattern to the standard SQL join. In this paper, we propose two primitives to enable efficient translation from SPARQL queries to MapReduce jobs. We use multiple-join-with-filter to substitute traditional SQL multiple join when feasible, and merge different stages in the query workflow. The evaluation on social network data benchmarks shows that the translation based on these two primitives can achieve up to 2x speedup in query running time comparing to the traditional two layer scheme.
Starting Page 27
Ending Page 32
Page Count 6
File Format PDF
ISBN 9781450321778
DOI 10.1145/2491159.2491169
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-08-16
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Sparql Query processing Rdf Mapreduce
Content Type Text
Resource Type Article
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