Efficient Aggregation Algorithms for Compressed Data Warehouses
IEEE Transactions on Knowledge and Data Engineering
Temporal and spatio-temporal aggregations over data streams using multiple time granularities
Information Systems - Special issue: Best papers from EDBT 2002
Spatiotemporal Aggregate Computation: A Survey
IEEE Transactions on Knowledge and Data Engineering
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Towards Using Data Aggregation Techniques in Ubiquitous Computing Environments
PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
LGeDBMS: a small DBMS for embedded system with flash memory
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Computers and Electronics in Agriculture
Specification-based data reduction in dimensional data warehouses
Information Systems
FAME-DBMS: tailor-made data management solutions for embedded systems
SETMDM '08 Proceedings of the 2008 EDBT workshop on Software engineering for tailor-made data management
A rule-based tool for gradual granular data aggregation
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
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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.