Abstract This paper provides an in-depth survey on the integration of machine learning and array databases. First,machine learning support in modern database management systems is introduced. From straightforward implementations of linear algebra operations in SQL to machine learning capabilities of specialized database managers designed to process specific types of data, a number of different approaches are overviewed. Then, the paper covers the database features already implemented in current machine learning systems. Features such as rewriting, compression, and caching allow users to implement more efficient machine learning applications. The underlying linear algebra computations in some of the most used machine learning algorithms are studied in order to determine which linear algebra operations should be efficiently implemented by array databases. An exhaustive overview of array data and relevant array database managers is also provided. Those database features that have been proven of special importance for efficient execution of machine learning algorithms are analyzed in detail for each relevant array database management system. Finally, current state of array databases capabilities for machine learning implementation is shown through two example implementations in Rasdaman and SciDB.

Machine Learning is increasingly being applied to many different application domains. From cancer detection to weather forecast, a large number of different applications leverage machine learning algorithms to get faster and more accurate results over huge datasets. Although many of these datasets are mainly composed of array data, a vast majority of machine learning applications do not use array databases. This tutorial focuses on the integration of machine learning algorithms and array databases. By implementing machine learning algorithms in array databases users can boost the native efficient array data processing with machine learning methods to perform accurate and fast array data analytics.

This paper describes a conceptual solution for heterogeneous sensor data integration in crowdsensing applications and one experimental implementation for a health monitoring system in an educational environment using a low cost hardware solution. Three kinds of protocols are integrated in this solution: HL7 for medical data, Observations and Measurements model for environmental data and BACnet for buildings monitoring. This last protocol has the particularity that manages sensoring and acting. A Common Data Model is described for the integration of three kinds of data and protocols, and a validation test application is described.