Graph databases for large-scale healthcare systems: A framework for efficient data management and data services
2014 IEEE 30th International Conference on Data Engineering Workshops (ICDEW) (2014)
Chicago, IL, USA
March 31, 2014 to April 4, 2014
Yubin Park , University of Texas at Austin, TX, USA
Mallikarjun Shankar , Oak Ridge National Laboratory, TN, USA
Byung-Hoon Park , Oak Ridge National Laboratory, TN, USA
Joydeep Ghosh , University of Texas at Austin, TX, USA
Designing a database system for both efficient data management and data services has been one of the enduring challenges in the healthcare domain. In many healthcare systems, data services and data management are often viewed as two orthogonal tasks; data services refer to retrieval and analytic queries such as search, joins, statistical data extraction, and simple data mining algorithms, while data management refers to building error-tolerant and non-redundant database systems. The gap between service and management has resulted in rigid database systems and schemas that do not support effective analytics. We compose a rich graph structure from an abstracted healthcare RDBMS to illustrate how we can fill this gap in practice. We show how a healthcare graph can be automatically constructed from a normalized relational database using the proposed “3NF Equivalent Graph” (3EG) transformation. We discuss a set of real world graph queries such as finding self-referrals, shared providers, and collaborative filtering, and evaluate their performance over a relational database and its 3EG-transformed graph. Experimental results show that the graph representation serves as multiple de-normalized tables, thus reducing complexity in a database and enhancing data accessibility of users. Based on this finding, we propose an ensemble framework of databases for healthcare applications.
Y. Park, M. Shankar, B. Park and J. Ghosh, "Graph databases for large-scale healthcare systems: A framework for efficient data management and data services," 2014 IEEE 30th International Conference on Data Engineering Workshops (ICDEW), Chicago, IL, USA, 2014, pp. 12-19.