Table2Graph: A Scalable Graph Construction From Relational Tables using Map-Reduce
Sangkeun Lee · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2016
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.