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Opensource & Machine Learning for GDPR Data Discovery

#artificialintelligence

GDPR (EU General Data Protection Regulation) is around the corner and bigger companies are getting ready to adopt it as they already know what kind of penalties come from non-compliance. It replaces replaces the Data Protection Directive 95/46/EC and was designed to harmonize data privacy laws across Europe and it is the biggest change on data privacy regulation in 20 years for Europe. While GDPR main elements can be a little tricky to understand, one thing is clear as sensitive Data Discovery is mandatory, so you can find the Personal and sensitive information on your data repositories, that can be almost everything from databases to files. Basically, we focus our data discovery on three main areas: column discovery, data discovery and file discovery. Column discovery is easy to understand, based on specific keywords or sentences we find column names on databases and match it with possible sensitive data.



Toward Unsupervised Activity Discovery Using Multi Dimensional Motif Detection in Time Series

AAAI Conferences

This paper addresses the problem of activity and event discovery in multi dimensional time series data by proposing a novel method for locating multi dimensional motifs in time series. While recent work has been done in finding single dimensional and multi dimensional motifs in time series, we address motifs in general case, where the elements of multi dimensional motifs have temporal, length, and frequency variations. The proposed method is validated by synthetic data, and empirical evaluation has been done on several wearable systems that are used by real subjects.


How Mathematical Discoveries are Made

@machinelearnbot

In one of my previous articles, you can learn the process about how discoveries are made by research scientists, from exploratory analysis, testing, simulations, data science guesswork, all the way to the discovery of a new theory and state-of-the-art statistical modeling,including new, fundamental mathematical/statistical equations.


How Mathematical Discoveries are Made

@machinelearnbot

In one of my previous articles, you can learn the process about how discoveries are made by research scientists, from stating the problem, exploratory analysis, testing, simulations, data science guesswork, all the way to the discovery of a new theory and state-of-the-art statistical modeling,including new, fundamental mathematical/statistical equations.