| To a large extent, oil enterprises are the knowledge-intensive enterprises. Because of thedata importance in enterprises· asset, it must be of high quality. Only the high-quality datacould be used for enterprise production, management decision and scientific research, whilegarbage data have no value for enterprise. How to improve and guarantee the data quality ofenterprise is always the hot topic in international and domestic research. Improving the dataquality is not only the problem which should be solved at the technical level, but alsoimportant at the management level. Therefore, only from both technical and managementlevel could fundamentally solve the problem of enterprise data quality.Firstly, based on the management demand of data quality in the data center of oildownhole job, research of data quality constraint rule is taken from the technical level. Andthirteen kinds of data quality constraint rule are defined. According to these constraint rules,employing the meta-data technology, data items of the downhole job data center are definedby the constraint rules. Then, in order to realize the data quality assessment of data center,seven kinds of data quality assessment indicators are defined. And, employing the meta-datatechnology, the meta-model, which is used for describing and storing these assessmentindicators, is built.Secondly, from the management level, data ccollection, data audit, data quality and otheraspects are analyzed and studied. And the monitoring process of data quality and somemeta-data model are constructed. This all realizes the management and monitoring of dataquality from management level.Then, the entire monitoring architecture model of data quality is constructed, whichincludes data layer, meta-data layer, data quality monitoring layer and presentation layer. Allthe layers are defined in detail. Through the architecture model, data quality monitoring isdistributed to three data processing stages, including collection, audit and quality assessment.The collection stage uses the data quality constraint rules to monitor data. The audit stage isused for the data audit. And through the detection of abnormal data, especially the numericaldata, the accuracy of the audit is improved. The quality assessment stage is to assess thequality of data center data through the data quality assessment indicators and get theassessment result of quality analysis.Finally, combined with the data quality monitoring demand of downhole job data center,the data center monitoring system is designed and developed based on the research above. The system realizes not only the entire monitoring of data quality in data center, but also theassessment of data quality. And it is preliminary applied in the data center project of DaqingOilfield Downhole Service Branch Company. The results show that the system has goodapplicability. |
