Outlier Detection From ETL Execution Trace
Extract, Transform, Load (ETL) is an integral part of Data Warehousing (DW) implementation. The commercial tools that are used for this purpose captures lot of execution trace in form of various log files with plethora of information. However, there has been hardly any initiative where any proactive analyses have been done on the ETL logs to improve their efficiency. In this paper, the authors utilize outlier detection technique to find the processes varying most from the group in terms of execution trace. As their experiment was carried on actual production processes, any outlier they would consider as a signal rather than a noise.