Distributed Graph Indexing and Query Processing Using Map-Reduce
Fathimabi Shaik
Fathimabi Shaik, VR Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 21-29 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10050275S419/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent times, we are observing that the of the size of the graph data is increasing and we cannot able to process by using a single machine in less time. In a distributed environment many users are giving the graph queries to get required data from large graph database. It becomes hard to get relevant graph data from a huge graph database. This paper address the issue of processing hundreds of query graphs from a huge graph database using distributed computing framework like Map-Reduce. We design a method to solve the problem of multiple graph query processing using inverted edge index and index maintenance. We develop a DIstributed Graph Indexing and Multiple Graph Query Processing Algorithm called DIGIMAP. DIGIMAP uses Replicated Join technique of Map-Reduce to filter the graphs and to do index maintenance. We did experiments using real-world graph datasets shows this approach improves the performance and quick processing of multiple graph queries over big dataset of graphs.
Keywords: Graph Query; Graph Database; Big Data; Parallel Processing; Map-Reduce; Distributed Graph Query Processing; Join Technique.
Scope of the Article: Neural Information Processing