Big Data guided Resources Businesses - Leveraging Location Analytics and Managing Geospatial-temporal Knowledge

Big Data guided Resources Businesses - Leveraging Location Analytics and Managing Geospatial-temporal Knowledge

Big data

Data warehouses

Decision support systems

Knowledge management

Location

Metadata

Location data rapidly grow with fast-changing logistics and business rules. Due to fast-growing business ventures and their diverse operations locally and globally, location-based information systems are in demand in resource industries. Data sources in these industries are spatial-temporal, with petabytes in size. Managing volumes and various data in periodic and geographic dimensions using the existing modelling methods is challenging. The current relational database models have implementation challenges, including the interpretation of data views. Multidimensional models are articulated to integrate resource databases with spatial-temporal attribute dimensions. Location and periodic attribute dimensions are incorporated into various schemas to minimise ambiguity during database operations, ensuring resource data's uniqueness and monotonic characteristics. We develop an integrated framework compatible with the multidimensional repository and implement its metadata in resource industries. The resources' metadata with spatial-temporal attributes enables business research analysts a scope for data views' interpretation in new geospatial knowledge domains for financial decision support. 2023 IEEE Computer Society. All rights reserved.

5008-5017

2023-January

Nimmagadda, Shastri L.

Ochan, Andrew

Reiners, Torsten

Mani, Neel

56th Annual Hawaii International Conference on System Sciences, HICSS 2023, January 3, 2023 - January 6, 2023

2023

conferencePaper

15301605