Reactive Company Control in Company Knowledge Graphs

• Main Authors: Magnanimi, Davide, Bellomarini, Luigi, Ceri, Stefano, Martinenghi, Davide
• Format: Conference Proceeding
• Language: English
• Subjects: Companies; company control; Datalog; Economics; Europe; knowledge graphs; Legged locomotion; materialization maintenance; Production; reactive reasoning; Regulators; Scalability
• ISSN: 2375-026X
• DOI: 10.1109/ICDE55515.2023.00256

The Company Control Problem consists in understanding who exerts decision power in companies. Central banks, financial intelligence units, and market regulators are all interested in this problem, which is crucial for their core goals. In the context where these actors operate, changes in company control call for immediate reactions.Yet, computing control relationships is a computationally expensive problem that involves traversing the entire shareholding structure and aggregating shares over multiple paths.In the context of the joint European banking supervision, the Bank of Italy will soon handle the shareholding graph of all European companies, which comprises hundreds of millions of entities (firms and individuals) and billions of edges and properties. This graph is highly volatile as the Bank continuously receives updates about shareholding relationships with unpredictable high frequency. This makes the straightforward bulk solution, where all the company control relationships are computed and materialized whenever a change occurs, unaffordable in practice.In this work, we present an incremental rule-based formalization of the problem, adopting the Vadalog fragment of the Datalog+/- families of languages. Our approach analyzes the specific change, singles out the portions of the graph that are affected by it, and selectively updates them. This allows one both to timely evaluate the impact of ownership variations on an extensive European-scale shareholding graph and to enable economists to perform the so-called "what-if analysis", i.e., simulation scenarios to proactively study the consequences of potential share acquisition operations, that currently are prohibitively time expensive. We provide an extensive experimental evaluation on very large company graphs, comparatively confirming the scalability of our technique in a real production setting.