Redundancy in Linked Data Partitioning for Efficient Query Evaluation

The problem of efficient querying large amount of linked data using Map-Reduce is investigated in this paper. The proposed approach is based on the following assumptions: a) Data graphs are arbitrarily partitioned in the distributed file system is such a way that replication of data triples between...

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Bibliographic Details
Published in2015 3rd International Conference on Future Internet of Things and Cloud pp. 497 - 504
Main Authors Kalogeros, Eleftherios, Gergatsoulis, Manolis, Damigos, Matthew
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2015
Subjects
Algorithm design and analysis
Cloud Computing
Distributed databases
Electronic mail
File systems
Graph Querying
Linked data
Map-Reduce
Nickel
Partitioning algorithms
Resource description framework
Semantic Web
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DOI10.1109/FiCloud.2015.36

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Summary:The problem of efficient querying large amount of linked data using Map-Reduce is investigated in this paper. The proposed approach is based on the following assumptions: a) Data graphs are arbitrarily partitioned in the distributed file system is such a way that replication of data triples between the data segments is allowed. b) Data triples are replicated is such a way that answers to a special form of queries, called subject-object star queries, can be obtained from a single data segment. c) Each query posed by the user, can be transformed into a set of subject-object star sub queries. We propose a one and a half phase, scalable, Map-Reduce algorithm that efficiently computes the answers of the initial query by computing and appropriately combining the sub query answers. We prove that, under certain conditions, query can be answered in a single map-reduce phase.
DOI:10.1109/FiCloud.2015.36