Abstract
This paper presents the concept of kernels to address the complexity of solving big-data applications. Their solution strategies often require evaluating domain-dependent subspaces on the big data and selecting the best result. As the data space in these problems is so vast that it is infeasible to scan the data once, we need domain-specific methods for identifying promising and manageable subspaces with a high density of solutions before looking for individual ones. To this end, we introduce kernels to represent some properties of the statistical quality, average density, or probability of solutions in a subspace to prune subspaces with suboptimal kernels. We classify various past approaches based on their analysis methods and illustrate each by an example. Our results confirm that kernels can effectively harness the complexity of solving big-data applications.
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