Building a Graph Database for Electronic Inverse Auction: Ecuador’s Case Study

In response to the significant impact of public procurement on Ecuador’s economic market, Ecuador’s law encourages the use of Electronic Reverse Auction (ERA) to promote competition between suppliers offering goods and services to the state. However, under auction conditions, both buyers and suppliers have been able to find ways to violate the rules and principles established to mitigate the risks of corruption prevalent in public procurement. These violations often leave behind a trail of evidence in the data captured by electronic means during the conduct of reverse auctions. However, when this information is published, scraped, and stored in an SQL database, users lose the ability to visualize patterns and anomalies among the relationships that exist in this highly relationship-centric domain. In this light, we have identified the need to transform the way procurement data is stored and processed by building a graph for the Electronic Reverse Auction. In the data model, the ERA processes that have been awarded by the network entities are grouped according to their market. Concurrently, each Contract aggregates the Suppliers who participated in the auction process and is linked, through a different edge, to the participant that was awarded the Contract. All these suppliers, regardless of whether they win or lose, are connected to the bids they submitted during the auction process and, if applicable, to the stakeholders who possess partial ownership in these companies. This paper describes the methodology used to establish these nodes and relationships in Neo4j, a graph database management system that currently stores approximately 365 thousand objects and 787 thousand connections belonging to Ecuador’s Electronic Reverse Auction network from 2008-03-24 to 2022-09-15.