Abstract: A dataset management system (“system”) reduces the amount of data to be stored for future analyses. The system determines a sampling rate of the data based on a required level of accuracy, and samples the data at the determined sampling rate. Initially, all data transactions (“full dataset”) and the sampled data (“sampled dataset”) are logged and stored. Based upon a trigger condition, e.g., after a specified period, the full dataset and the sampled dataset are analyzed separately and the analysis results are compared. If the comparison is sufficiently similar (i.e., the sampling produces a sufficiently accurate set of data or a variance between the analysis results of the datasets is within a specified threshold), the system discontinues full data logging and stores only the sampled dataset. Further, the full dataset is deleted. The sampling thus reduces the required data volume significantly, thereby minimizing consumption of the storage space.

Abstract: A dataset management system (“system”) reduces the amount of data to be stored for future analyses. The system determines a sampling rate of the data based on a required level of accuracy, and samples the data at the determined sampling rate. Initially, all data transactions (“full dataset”) and the sampled data (“sampled dataset”) are logged and stored. Based upon a trigger condition, e.g., after a specified period, the full dataset and the sampled dataset are analyzed separately and the analysis results are compared. If the comparison is sufficiently similar (i.e., the sampling produces a sufficiently accurate set of data or a variance between the analysis results of the datasets is within a specified threshold), the system discontinues full data logging and stores only the sampled dataset. Further, the full dataset is deleted. The sampling thus reduces the required data volume significantly, thereby minimizing consumption of the storage space.