An Efficient Storage Scheme for Sustainable Development Goals Data Over Distributed Knowledge Graph Stores

Abstract

The achievement of the Sustainable Development Goals (SDGs) is important in order to ensure a world worth living in for future generations. Digitization and the plethora of data available for analysis offer new opportunities to support and monitor the achievement of the SDGs. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of data, as intended by this work. In this paper, we propose dimensionality reduction methods to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. In particular, our work proposes and experimentally corroborates the use of Hilbert Space Filling Curves (HSFCs) to efficiently store real SDG data with reduced retrieval times and preservation of their semantic closeness. First, algorithm is theoretically founded and explained and an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our algorithm. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.