very hard to estimate the selectivity of the conjunct in general. Poor cardinality estimates and uncaught correlation are one of the main reasons why query optimizers pick poor query plans. This is one reason why a database administrator should regularly update the database statistics, especially after major data loads/unloads. Extensions Classical query optimization assumes that query plans are compared according to one single cost metric, usually execution time, and that the cost of each query plan can be calculated without uncertainty. Both assumptions are sometimes violated in practice and multiple extensions of classical query optimization have been studied in the research literature that overcome those limitations. Those extended problem variants differ in how they model the cost of single query plans and in terms of their optimization goal. Parametric query optimization Classical query optimization associates each query plan with one scalar cost value. Parametric query optimization assumes that query plan cost depends on parameters whose values are unknown at optimization time. Such parameters can for instance represent the selectivity of query predicates that are not fully specified at optimization time but will be provided at execution time. Parametric query optimization therefore associates each query plan with a cost function that maps from a multi-dimensional parameter space to a one-dimensional cost space. The goal of optimization is usually to generate all query plans that could be optimal for any of the possible parameter value combinations. This yields a set of relevant query plans. At run time, the best plan is selected out of that set once the true parameter values become known. The advantage of parametric query optimization is that optimization (which is in general a very expensive operation) is avoided at run time. Multi-objective query optimization There are often other cost metrics in addition to execution time that are relevant to compare query plans. In a cloud computing scenario for instance, one should compare query plans not only in terms of how much time they take to execute but also in terms of how much money their execution costs. Or in the context of approximate query optimization, it is possible to execute query plans on randomly selected samples of the input data in order to obtain approximate results with reduced execution overhead. In such cases, alternative query plans must be compared in terms of their execution time but also in terms of the precision or reliability of the data they