To ease the proliferation of big data, it frequently is transformed, be it by compression, be it by anonymization. Such transformations however modify characteristics of the data, such as changes in the case of time series. Changes however are important for subsequent analyses. The impact of those modifications depends on the application scenario, and quantifying it is far from trivial. This is because a transformation can shift or modify existing changes or introduce new ones. In this paper, we propose MILTON, a flexible and robust Measure for quantifying the Impact of Lossy Transformations on subsequent change detectiON. MILTON is applicable to any lossy transformation technique on time-series data and to any general-purpose change-detection approach. We have evaluated it with three real-world use cases. Our evaluation shows that MILTON allows to quantify the impact of lossy transformations and to choose the best one from a class of transformation techniques for a given application scenario.

Correlation analysis is one of the key elements of statistics, and has various applications in data analysis. Whereas most existing measures can only detect pairwise correlations between two dimensions, modern analysis aims at detecting correlations in multi-dimensional spaces. We propose MAC, a novel multivariate correlation measure designed for discovering multi-dimensional patterns. It belongs to the powerful class of maximal correlation analysis, for which we propose a generalization to multivariate domains. We highlight the limitations of current methods in this class, and address these with MAC. Our experiments show that MAC outperforms existing solutions, is robust to noise, and discovers interesting and useful patterns.