If you are one of those people who must understand what makes things tick, the new whitepaper “The Architecture and Motivation for SciDB” is for you.

In 2010 a group of database researchers and engineers led by Turing Award winner, Paradigm4 CTO, and MIT Professor Michael Stonebraker surveyed a large number of scientists to find out what kind of solution they wanted to help manage and analyze their data. Their answers formed the design goals for SciDB, a computational, massively parallel, transactional, array-oriented, Database Management System.

This paper presents a high level description of SciDB and explores the design of some of its critical components in detail. You will learn what SciDB does, how SciDB is implemented, and why these technical choices make SciDB excellent for managing and analyzing large, diverse, quantitative data sets, especially machine-generated data – measurements and observations from instruments, sensors, and other data acquisition or monitoring devices. As the Internet of Things spreads from commercial to industrial to biomedical, such data will only become significantly more voluminous and pervasive.

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