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Ability to easily combine the data from diverse sources in a single analytical workflow is one of the greatest promises of the Big Data\udtechnologies. However, such integration is often challenging as datasets originate from different vendors, governments, and research\udcommunities that results in multiple incompatibilities including data representations, formats, and semantics. Semantics differences are\udhardest to handle: different communities often use different attribute definitions and associate the records with different sets of evolving\udgeographic entities. Analysis of global socioeconomic variables across multiple datasets over prolonged time is often complicated by the\uddifference in how boundaries and histories of countries or other geographic entities are represented. Here we propose an event-based data\udmodel for depicting and tracking histories of evolving geographic units (countries, provinces, etc.) and their representations in disparate\uddata. The model addresses the semantic challenge of preserving identity of geographic entities over time by defining criteria for the entity\udexistence, a set of events that may affect its existence, and rules for mapping between different representations (datasets). Proposed model\udis used for maintaining an evolving compound database of global socioeconomic and environmental data harvested from multiple sources.\udPractical implementation of our model is demonstrated using PostgreSQL object-relational database with the use of temporal, geospatial,\udand NoSQL database extensions

SPATIO-TEMPORAL DATA MODEL FOR INTEGRATING EVOLVING NATION-LEVEL DATASETS