A New Approach for Efficient Structure Discovery in IoT

Complex, multivariate data streams frequently comprise subjacent behavioral patterns, which are subsumable by a process of statistical structure discovery. Revealing these hidden patterns from raw data is a major challenge in abstracting information and thus for new opportunities of efficient data analysis at scale. State-of-the-art approaches, such as CKS and ABCD, leverage statistical data models and Gaussian Processes in order to abstract from raw data and to describe their major data characteristics by means of kernel-decomposed covariance functions. The process of identifying the most appropriate covariance function is a performance bottleneck due to its super-quadratic computation time complexity for model selection and evaluation. In this paper, we thus propose a new approach for the computation of large-scale statistical data models. To this end, we propose to bound the complexity of the statistical data model and develop a sequential agglomerative approach to reduce the computational load of the required evaluative calculations. Our performance analysis indicates that our proposal is able to outperform state-of-the-art kernel search algorithms such as CKS and ABCD with respect to the qualities of efficiency and accuracy.