Apache Calcite 
Apache Calcite is a Dynamic Data Management Framework. It Contains Many of the Pieces That Comprise a Typical Database Management System, but Omits Some key Functions: Storage of Data, Algorithms to Process Data, and a Repository for Storing Metadata. Calcite Intentionally Stays out of the Business of Storing and Processing Data. As we Shall See, This Makes it an Excellent Choice for Mediating Between Applications and one or More Data Storage Locations and Data Processing Engines. It is Also a Perfect Foundation for Building a Database: Just add Data.
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources …
Mountain Plot
A mountain plot (or “folded empirical cumulative distribution plot”) is created by computing a percentile for each ranked difference between a new method and a reference method. To get a folded plot, the following transformation is performed for all percentiles above 50: percentile = 100 – percentile. These percentiles are then plotted against the differences between the two methods (Krouwer & Monti, 1995). The mountain plot is a useful complementary plot to the Bland & Altman plot. In particular, the mountain plot offers the following advantages:· It is easier to find the central 95% of the data, even when the data are not Normally distributed.· Different distributions can be compared more easily. …
Replacement AutoEncoder
An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially sensitive information about the individuals can also be discovered through these datasets and this cannot easily be protected using traditional privacy approaches. In this paper, we propose an integrated sensing framework for managing access to personal time-series data in order to provide utility while protecting individuals’ privacy. We introduce \textit{Replacement AutoEncoder}, a novel feature-learning algorithm which learns how to transform discriminative features of multidimensional time-series that correspond to sensitive inferences, into some features that have been more observed in non-sensitive inferences, to protect users’ privacy. The main advantage of Replacement AutoEncoder is its ability to keep important features of desired inferences unchanged to preserve the utility of the data. We evaluate the efficacy of the algorithm with an activity recognition task in a multi-sensing environment using extensive experiments on three benchmark datasets. We show that it can retain the recognition accuracy of state-of-the-art techniques while simultaneously preserving the privacy of sensitive information. We use a Generative Adversarial Network to attempt to detect the replacement of sensitive data with fake non-sensitive data. We show that this approach does not detect the replacement unless the network can train using the users’ original unmodified data. …
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