The effects of normalisation on the ability of business end-users to detect data anomalies: An experimental evaluation

With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.

Cigarette smoking and risk of epithelial ovarian cancer (Australia)

Objectives: We studied the association between cigarette smoking and ovarian cancer in a population-based case-control study. Methods: A total of 794 women with histologically confirmed epithelial ovarian cancer who were aged 18-79 years and resident in one of three Australian states were interviewed, together with 855 controls aged 18-79 years selected at random from the electoral roll from the same states. Information was obtained about cigarette smoking and other factors including age, parity, oral contraceptive use, and reproductive factors. We estimated the relative risk of ovarian cancer associated with cigarette smoking, accounting for histologic type, using multivariable logistic regression to adjust for confounding factors. Results: Women who had ever smoked cigarettes were more likely to develop ovarian cancer than women who had never smoked (adjusted odds ratio (OR) = 1.5; 95% confidence interval (CI) = 1.2-1.9). Risk was greater for ovarian cancers of borderline malignancy (OR = 2.4; 95% CI = 1.4-4.1) than for invasive tumors (OR = 1.7; 95% CI = 1.2-2.4) and the histologic subtype most strongly associated overall was the mucinous subtype among both current smokers (OR = 3.2; 95% CI = 1.8-5.7) and past smokers (OR = 2.3; 95% CI = 1.3-3.9). Conclusions: These data extend recent findings and suggest that cigarette smoking is a risk factor for ovarian cancer, especially mucinous and borderline mucinous types. From a public health viewpoint, this is one of the few reports of a potentially avoidable risk factor for ovarian cancer.