Table2Graph: A scalable graph construction from relational tables using map-reduce

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Identifying correlations and relationships between entities within and across different data sets (or databases) is of great importance in many domains. The data warehouse-based integration, which has been most widely practiced, is found to be inadequate to achieve such a goal. Instead we explored an alternate solution that turns multiple disparate data sources into a single heterogeneous graph model so that matching between entities across different source data would be expedited by examining their linkages in the graph. We found, however, while a graph-based model provides outstanding capabilities for this purposes, construction of one such model from relational source databases were time consuming and primarily left to ad hoc proprietary scripts. This led us to develop a reconfigurable and reusable graph construction tool that is designed to work at scale. In this paper, we introduce Table2Graph, the graph construction tool based on Map-Reduce framework over Hadoop. We also discuss results from applying Table2Graph to integrate disparate healthcare databases.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 1st International Conference on Big Data Computing Service and Applications, BigDataService 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages294-301
Number of pages8
ISBN (Electronic)9781479981281
DOIs
StatePublished - Aug 10 2015
Event1st IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2015 - San Francisco, United States
Duration: Mar 30 2015Apr 3 2015

Publication series

NameProceedings - 2015 IEEE 1st International Conference on Big Data Computing Service and Applications, BigDataService 2015

Conference

Conference1st IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2015
Country/TerritoryUnited States
CitySan Francisco
Period03/30/1504/3/15

Keywords

  • Construction
  • ETL
  • Graph
  • Heterogeneous
  • Map-Reduce

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