Intelligence Method Of Data Maintenance

How to improve the data maintenance, data quality assurance are important issues placed in a database application. For data quality issues, database users mostly strengthen the construction of systems, increase staff accountability, and take other measures to improve data quality from the enterprise internal management systems perspective. Database technology manufacturers strive to provide common data quality product solutions, provide most of the methods based on the technical level, which lacks of specific users with complex business logic of the effective combination. Some domestic research institutes have also raised the quality of data on the settlement of the theory. They believe the data quality management as the same product quality management throughout the life cycle of data in all phases. However, at present it lacks of a system of thinking. Most domestic and international researchers focus on the data warehouse and data mining systems in the areas of data quality issues, which were after the event techniques. There are many massive data cleaning researches, but they cannot directly improve the quality of the data itself. They can only reduce unreliable data to affect the decision-making and fail to resolve the fundamental problem. For data quality monitoring and maintenance of data, effort should be moved to before the acquisition, reproduction period.Common standards of data quality are: data integrity, consistency and accuracy. For single-source data information systems to improve the quality of the data is difficult, particularly in the accuracy of the data. When we cannot judge the accuracy of the data from the logic, this thesis introduces mathematical statistical methods to determine the accuracy of data accuracy, thereby discovering data problems.Data Maintenance is usually after the data occurs problems, through information management systems or database management system for data adjustment and revisions to preserve its normal state. This format enables the data maintenance always in a passive position. This thesis introduces Agent technology to set data maintenance into the system normal operation. It finds data problems in real-time, take the initiative to find the problem and using standardized methods to deal with the data problems. This increases data maintenance work efficiency and MTBF (Mean time Before Failure) of Information System.This thesis, based on CTAIS (China Tax Administrator Information System), describes the implementation process of intelligent data elaborated safeguard. At a strategic level, the author offers active data maintenance mode, change data maintenance ideas. Data Maintenance will work through the day-to-day operation of the system process. Prevent maintaining data work from increasing over time in linear growth trends. Allow the system "lubricant" while running. It will increase the average time to failure, and greatly improve efficiency. Identify data problems early and the approach both standard and feasible. Data maintenance workload is at a predictable, controllable condition. Data maintenance sets in system operations and forms a closed-loop feedback system, changing data maintenance concept. For the results of the operation of data maintenance system, it is an objective, quantitative evaluation on the existing system. Under the premise of not increase the complexity of business logic level improvs the operating results. It is a good complement of the existing system. The strategy used and the method of information systems for data maintenance has a useful generalization.