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(UserIdPk)entityContext.getPrimaryKey(); StringBuffer sql = new StringBuffer("SELECT * FROM ADDRESS WHERE user_id="); sql.append(pk); rs = stmt.executeQuery(sql.toString()); addressValueObject = new AddressValueObject(); if(rs.next()) { addressValueObject.street = rs.getString("street"); addressValueObject.suburb = rs.getString("suburb"); addressValueObject.areaCode = rs.getString("areaCode"); addressValueObject.state = rs.getString("state"); addressValueObject.country = rs.getString("country"); addressValueObject.lastUpdated = rs.getTimestamp("last_update"); } } catch(SQLException se) { } finally { try { rs.close(); }catch(SQLException se) { } try { stmt.close(); }catch(SQLException se) { } try { conn.close(); }catch(SQLException se) { } } }

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To illustrate these methods we use data from an online weight loss study conducted by Kaiser Permanente, the analysis of which is discussed in Couper et al (2005) Approximately 4000 subjects were randomly assigned to the treatment or the control group after they filled out an initial survey, which contained baseline measurements such as weight, motivation to weight loss, etc For the treatment group, the weight loss information provided online was tailored to the subjects based on their answers to the initial survey; for the control group, information provided online was the same for all the subjects but they could search for additional weight loss information using hyperlinks in the text At 3, 6 and 12 months, follow-up invitations for web surveys were sent to the participants, which collected measurements such as current weight.

cal.set(java.util.Calendar.YEAR, 2001); cal.set(java.util.Calendar.MONTH, java.util.Calendar.DECEMBER); cal.set(java.util.Calendar.DATE, 12); cal.add(java.util.Calendar.DATE, 91); out.print(cal.getTime().toString());

There are many options for delivering data analysis or data mining solutions. Some of the more popular include: Report: A report describing the business intelligence derived from the project is a common deliverable. The report should be directed to the persons responsible for making decision. It should focus on signi cant and actionable items, that is, it should be possible to translate any conclusions into a decision that can be used and that makes a difference. It is increasingly common for the report to be delivered through the corporate intranet to enable additional interested parties to bene t from the report. Integration into existing systems: The integration of the results into existing operational systems or databases is often one of the most cost effective approaches to delivering a solution. For example, when a sales team requires the results of a predictive model, that ranks potential customers on the basis of the likeliness that they will buy a particular product, the model may be integrated with the CRM system (Customer Relationship Management) that they currently use on a daily basis. This minimizes the need for training and makes the deployment of the results easier. Prediction models or data mining results can also be integrated into system accessible to a customers such as e-commerce web sites. In this situation, customers may be presented with additional products or services that they may be interested

Our goal is to compare short-term and long-term treatment effects; in particular, we compare the reduction of the body mass index (BMI), defined as the difference of the follow-up BMI and the baseline BMI There were 2059 subjects in the treatment group and 1956 subjects in the control group at baseline At 3 months, 623 subjects in the treatment group and 611 subjects in the control group responded to the second survey At 6 months, 438 subjects in the treatment group and 397 subjects in the control group remained in the study At 12 months, 277 subjects in the treatment group and 314 subjects in the control group responded to the last survey.

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Comparisons of the baseline measurements between subjects who remained in the study and those who dropped out at 3 months indicate that subjects who remained in the study have much lower baseline BMI than those who dropped out of the study for the treatment group (P < 0001), but this difference is not seen in the control group (P 5 047); On the other hand, subjects who remained in the study at 3 months have better baseline health, as shown by the incidence of previous disease, than those who dropped out of the study for the control group (P < 001), but this difference is not seen in the treatment group (P 5 056) Similarly, subjects who remained in the study at 6 months have much lower baseline BMI than those who dropped out of the study for the treatment group (P < 0.

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001), but this difference is not seen in the control group.