Gradual Data Aggregation in Multi-granular Fact Tables on Resource-Constrained Systems

Multi-granular fact tables are used to store and query data at different levels of granularity. In order to collect data in multi-granular fact tables on a resource-constrained system and to keep it for a long time, we gradually aggregate data to save space, however, still allowing analysis queries, for example, for maintenance purposes etc. to work and generate valid results even after aggregation. However, ineffective means of data aggregation is one of the main factors that can not only reduce performance of the queries but also leads to erroneous reporting. This paper presents effective methods for data reduction that are developed to perform gradual data aggregation in multi-granular fact tables on resource-constrained systems. With the gradual data aggregation mechanism, older data can be made coarse-grained while keeping the newest data fine-grained. This paper also evaluates the proposed methods based on a real world farming case study.