| Current society, more and more enterprises recognize that only through full use oftheir existing data can maximize their benefits. Most enterprises are not lack of data,but delay in data redundancy and inconsistency. Traditional database is abusiness-oriented which designed only to streamline the labor intensity of a specificoperation, and there is no corresponding system for the top leadership. Enterprisesneed new techniques to compensate for the inadequacies of the existing data systemand need to have an extensive collection of data integration to the data warehouse toextract useful information from business data to help them make timely and correctjudgment in business management and development.In this paper, in-depth Study of traditional data warehouse program, that thecurrent data storage system can not meet the needs of small-scale enterprises. Forthem, the quality of human resources staff are often unable to meet the managementand maintenance of the data warehouse processing needs due to limited manpowerand financial resources. Meanwhile the traditional data warehouse usually onlyhistorical data analysis, data extraction cycle is too long (even one day a week ingeneral) difficult to reflect market changes. From this perspective and thecharacteristics of small-scale enterprises, add automatically response and handlingcapability timely to data warehouse is in line with the characteristics and needs ofthem.This paper introduces small-scale-enterprise-oriented adaptive data warehouseafter describing ECA rules, analysis rules, and distributed agent. By adding the ECArules, intermediate data warehouse and distributed agent, the system overcomesdisadvantage of traditional data warehouse in handling granularity, handlingperformance, and makes the mining and analyzing capacity of data warehouse reaching a bidirectional real-time adaptive.Finally, the paper put forward some issues of small-scale-enterprise-orientedactive data warehouse which should be resolved in future. |
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