The need for data analysis patterns (in software engineering)
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When you call a doctor, you would expect that she would be prepared with a set of remedies for your disease. You would not be pleased to see her digging into a huge amount of clinical data while she makes a diagnosis and searches for a solution for your problem, neither would you expect her to propose a cure based on your case alone. The remedies she proposes are solutions to recurring problems that medical researchers identify by analyzing data of patients with similar symptoms and medical histories. Remedies are coded in a language that a doctor understands (eg, they tell when and how to treat a patient) and lead to meaningful conclusions for patients with the same disease (eg, they tell the probability that the disease will be defeated and eventually with which consequences). Once found, such solutions can be applied over and over again. With the repeated use of a solution, medical researchers can indeed gain knowledge on successes and failures of a remedy and provide meaningful conclusions to future patients thereafter. The remedy metaphor helps describe how data analysis patterns are used in empirical sciences. First, a pattern is a coded solution of a recurring problem. When a problem occurs several times, we accumulate knowledge on the problem and its solutions. With this knowledge, we are able to code a solution in some sort of modeling language that increases its expressivity and capability of re-use. Second, a pattern is equipped with a sort of measure of success of the solution it represents. The solution and the measure result from the analysis of historical data and provide actionable insight for future cases.