4. Table structure maintenance
Tools that maintenance table structures are provided, and they can be used to check the coherence between system table and oilfield related table.
The function that check over the structure of the basic tables will ensure the coherence between table structures and the data dictionary. If a data field in table can not find a related description in the data dictionary, this data field will be added to the data dictionary.If a description of a data field in the data dictionary doesn’t has related data field in a table, the data field will be added to the data table.Therefore, data integrity and coherence can be assured.

5. Data quality control:
Data quality control for oilfield information is a complex process which involves huge amount of work and we can not finish the work in one run. Data quality control will walk through the whole building process of database. And manual work is not enough, we must get help from related techniques to make it automatically done.
Data quality can be measured in the following standards:
Coherence: measure the semantics and use them;
Integrity: measure the difference of records;
Completeness: compare to the universal validity, make quantization measure over captured entities or events;
Deepness: measure the historical weight over entities or events;
Accuracy: measure the correctness of data;
Latent: measure a recorded time period;
Redundancy: measure the amount of unnecessary information;
This system uses meta data architecture to store the basic data obtained during quality analysis, these data include the semantic measures in the coherence analysis, the integrity standard in the integrity analysis, and the related limits and checks during data collection process. All of this is for a better data quality control.

Beijing Mandarin Chinese Translation Service Company has translated many technical documents in the field of Table structure maintenance.
华译网北京翻译公司翻译过大量有关数据表结构维护管理功能文件资料。

4、数据表结构维护管理功能
        提供系统数据表结构的维护工具，并可以进行对系统表与油田开发数据库标准结构的一致性检查。
        提供对基表作标准性检查的功能，以检查数据库中各表的结构是否符与数据字典中的描述一致：若数据表中的字段在表结构说明表中没有描述，则把该字段的描述添加到数据字典中。若数据字典中描述的字段在数据表中没有相应的字段，则在该数据表中增加相应的字段。如此处理可保证数据的完整性和一致性。

5、数据质量控制：
        油田开发数据质量控制是一个复杂的过程，工作量大，而且不是进行一次就完成的，需要贯穿整个数据库建设过程，也不是仅靠手工方式就可完成地，而必须通过相应的技术辅助，实现数据质量控制过程的自动化。
        数据质量可以用如下几个关键特性来衡量：
        一致性：用来测量并应用的语义标准；
        完整性：用来测量记录间的差距；
        整体性：与那些普遍有效性对比，进行捕获实体或事件的量化测评；
        深度性：测量实体或事件的历史量；
        准确性：用来测量数据正确性；
        潜伏性：用来测量一个记录的时间；
        冗余性：测量不必要的重复信息的量。
        系统采用元数据管理体系，来保存油田开发数据质量分析的基础数据，如一致性分析中涉及到的语义关系，完整性分析中衡量数据完整性的标准等，在数据的采集过程加入相关约束判断和校验，以实现数据质量的控制。