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The number of changes that IT departments have to deal with is growing at a fast pace in response to changing business needs of enterprises. As changes are getting executed and deployed, knowledge is being created and stored. It is of paramount importance to the success of the business to re-use that knowledge for future changes. In fact, those who do not learn from past experiences are doomed to repeat the same mistakes as well as not bear the fruit of the ones that were successful. This paper addresses this concern by providing for every change being worked out the most similar past changes. Our solution combines data mining and optimization paradigms to model the problem of finding past similar changes by designing and learning similarity functions. Our approach enhances the efficiency and effectiveness of dealing with changes, by reducing the risk and shortening the time of introducing new changes.