Navigating Immovable Assets: A Graph-Based Spatio-Temporal Data Model for Effective Information Management

Abstract

Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both spatial and temporal data remains a challenge. Therefore, this paper proposes a graph-based spatio-temporal data model to integrate spatial and temporal information into asset management. In the spatial layer, we provide a graph-based method that uses topological containment and connectivity relationships to model the interior building space using data from 3D city models. In the temporal layer, we proposed the Aggregated Directly-Follows Multigraph (ADFM), a novel process model based on a directly-follows graph (DFG), to show the chronological flow of events in asset management by taking into consideration the repetitive nature of events in asset management. The integration of both layers allows spatial, temporal, and spatio-temporal queries to be made regarding information about events in asset management. This method offers a more straightforward query, which helps to eliminate duplicate and false query results when assessed and compared with a flattened graph event log. Finally, this paper provides information for the management of 3D spaces using a NoSQL graph database and the management of events and their temporal information through graph modelling.

Keywords

• asset management
• 3D city models
• graph data model
• graph database
• spatio-temporal
• directly-follows graph