By Javier Machin UC ´master student and Karim Pichara, Young Researcher of MAS and Associate Professor at the Computer Science Department from Universidad Católica de Chile

In astronomy, there have been many attempts to gather and integrate numerous data sources to create a centralized, organized and consolidated database, hopefully in some cases with visualization and analysis mechanisms. Unfortunately, most of those attempts have been inefficient, mainly, because they lack a holistic scheme that allows for heterogeneous data integration, high-level pre-processing and visualization tools within the same structure.

Industries like Construction, Banking, Finance, Education, and Medicine, were facing similar problems back to the 1960’s. Enterprises started to collect a massive amount of data about customers and stock markets. They quickly notice the value in the data and developed successful decision-making systems based on complete data analysis platforms. The solution to this scenario was the development of Business Intelligent engines (BI) and Data Warehouses (DW).

In the last ten years, Astronomy has become a data-driven science due to the increasing capability of generating and collecting data as a result of massive observational projects that have delivered a significant amount of astronomical data.

Taking inspiration from industries mindset, myself and my student Javier Machin Matos, work on designing and implementing a “Global Data Warehouse for Astronomy” – GAWA – that allows users – Astronomer Researchers, Astronomers Teachers, and Astronomers Students – to visually process and analyze astronomical data in real-time, narrowing down exploratory analyses, and providing an effective Data-Astronomer interaction. Gawa provides an on demand set of algorithms to pass time series of data through, computes statistical tests, or “features” associated to the series, and gives as output a collection of graphic windows that are easy to analyze by an educated eye. In the attached figure, for example, the top row shows the global characteristics of the standard dispersion in brightness for normal stars (non variables) in the leftmost column, for RR Lyrae variable stars (middle column) and for gravitational lensing transients (rightmost column). The third row, on the other hand, makes it clear that both non-variable stars and RR Lyrae variables do show a strong auto-correlation at a given period, while microlensing transient events, which display light curves with a non-standard behavior, do not.  The readers may want to try the system, a prototype of which is publically available at http://isadoranun.github.io/tsfeat/FeaturesDocumentation.html#Feature-Analysis-for-Time-Series

The impact of GAWA is enormous, improve and promote a more collaborative research through an environment of transparent data access assist in their daily decision-making, with better adaptability, top flexibility, and best support, where students can naturally interact with the data while they are facing new concepts and theories. Teachers will provide the classrooms with an interactive environment that will enable students to try in real time what they learn in the class. The research community will have an easy way to share any analysis among collaborators, to select specific astronomical objects across different sources of data, to visualize particular patterns in objects of interest, and to put the efforts in the astronomical concepts rather than in low-level commands to manipulate the databases.

Main Image: GAWA: A Global Data Warehouse for Astronomy.