A rule-based tool for gradual granular data aggregation

In order to keep more detailed data available for longer periods, old data has to be reduced gradually to save space and improve query performance, especially on resource-constrained systems with limited storage and query processing capabilities. In this regard, some hand-coded data aggregation solutions have been developed; however, their actual usage have been limited, for the reason that hand-coded data aggregation solutions have proven themselves too complex to maintain. Maintenance need to occur as requirements change frequently and the existing data aggregation techniques lack flexibility with regards to efficient requirements change management. This paper presents an effective rule-based tool for data reduction based on gradual granular data aggregation. With the proposed solution, data can be maintained at different levels of granularity. The solution is based on high-level data aggregation rules. Based on these rules, data aggregation code can be auto-generated. The solution is effective, easy-to-use and easy-to-maintain. In addition, the paper also demonstrates the use of the proposed tool based on a farming case study using standard database technologies. The results show productivity of the proposed tool-based solution in terms of initial development time, maintenance time and alteration time as compared to a hand-coded solution.