Exception determination method and apparatus

Abstract

Embodiments of the invention provide an exception determination method and apparatus. The method comprises the steps of obtaining original reported data stored in a data warehouse and URL addresses corresponding to the original reported data; making statistics on the number of the original reported data corresponding to the URL addresses; searching for URL addresses with a data number beyond a preset threshold range; and determining original reported data corresponding to the found URL addresses as exceptional data. According to the method and apparatus, whether the original reported data corresponding to the URL addresses are exceptional or not is determined by searching for the original reported data corresponding to the URL addresses with the data number beyond the preset threshold range, so that the data exception can be detected from the source and the efficiency of locating data exception reasons can be improved.

Description

technical field [0001] The present disclosure relates to the field of computer technology, and in particular to a method and device for determining abnormality. Background technique [0002] The data warehouse is a subject-oriented and integrated data collection in enterprise management and decision-making, that is to say, for all application systems, such as customer relationship management systems, financial systems, etc., it is integrated according to the subject and records the entire historical change. It is a single data store created for analytical reporting and decision support purposes, providing business intelligence to guide business process improvement, monitor time, cost, quality, and control for businesses that require business intelligence. [0003] In practical applications, because ETL (Extract Transform Load, data warehouse technology) process often has some abnormal behaviors, it may cause some abnormal data to be stored in the data warehouse, which may le...

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Problems solved by technology

[0003] In practical applications, because ETL (Extract Transform Load, data warehouse technology) process often has some abnormal behaviors, it may cause some abnormal data to be stored in the data warehouse, which may lead to all operations based on these abnormal data Errors occur in the processing flow, in order to avoid this situation, an efficient and high-quality analysis and monitoring mechanism is required

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