How Graph Data and Ontology May Add Value to Transactional Data

Tracking #: 3130-4344

This paper is currently under review
Minchul Lee
Boonserm Kulvantunyou
Scott Nieman
Bongjun Ji1
Nenad Ivezic
Hyunbo Cho

Responsible editor: 
Guest Editors SW for Industrial Engineering 2022

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Full Paper
In the era of data-driven economy, businesses seek to make data smarter – easier to be analyzed for gaining insights – while deal-ing with numerous sources of the data. One data architecture for integrating data from these sources is the data lake. Data lake captures all the data crisscrossing the enterprise into a single repository for easy and low-cost access in real-time or near real-time without actively syncing data from the sources. During recent years, our industry partners who currently use traditional structured data standard in XML or JSON have posted the questions about the values of graph data and ontology. Therefore, this paper, primarily targeting industry practitioners such as enterprise architect and IT managers, investigates how transactional data stored in the data lake may be integrated and queried for business insights using the XML data versus graph and ontology data. The assumptions are that the raw data follow a common information exchange standard in XML syntax and the storage behind data lake is a NoSQL database. Three experiments were conducted on logistics data 1) using only NoSQL, native API to get to the query of interest; 2) translating raw XML data into graph data without introducing additional formal semantics beyond what is already available in the corresponding XML schemas and use SPARQL to get to the query of interest; and 3) introducing reason-er and additional formal semantics via an OWL ontology into the architecture and use OWL DL Query or SPARQL, which is based on the ontology, to get to the query of interest. While each experiment incurs increasing pre-processing efforts; their differ-ences and values are analyzed and discussed respectively.
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