The challenges related to data volume, variety and velocity are similar in astronomy and Earth observations, with computer science as the common denominator. The BIG SKY EARTH Action aims at boosting the communication within and between disciplines by identifying and clustering relevant common solutions developed within research and industrial environments. These solutions can be aided by methodologies and tools for large distributed data management and processing, developed by computer scientists in academia or industry.For example, metadata is extensively exploited in multimedia Digital Asset Management to provide effective access to deep repositories of audio-visual content. This approach can contribute a valuable know-how to natural scientists working with similar type of data structures in large databases. Visual Analytics is another example of a growing field in computer science, with interesting implications for astronomy and Earth observation that inherently depend on visual datasets.

Therefore, the objectives are set in a logical framework where a diverse network of experts identifies the issues to be addressed, followed by joint utilization of their existing resources to tackle the problems related to these issues, with the emphasis on building bridges between disciplines needed for success and disseminating the acquired knowledge, know-how and results to a wider circle of stakeholders.

• COST Action TD1403: see the COST official pages of these Action. It ended on Jan 12, 2019, but the networking activities are still alive.
• If you want to join our network, fill out THIS FORM to receive updates on the news and activities.
• Action is described in its Memorandum of Understanding
• Contact us at: info  @  bigskyearth.eu

OBJECTIVES

OBJECTIVE 1: Framing the Joint Long-term Agenda

Short description: identify, compare and assess the common narrative, methods, techniques and tools used in astro-, geo- and computer sciences. This includes, but is not limited to, discussing performance parameters of data archives, critical evaluation of optimal tools used by users, debating the optimal incorporation of emerging technologies and how to boost interdisciplinary education of young scientists.
The guiding question: “What are the common long-term topics of interest that can keep this collaboration alive after the end of this Action?”

OBJECTIVE 2: Incubation of New Knowledge

Short description: through collaboration develop new solutions to challengies facing astro- and geo-informatics. For example, implementation of a better DBMS for some data archive, or standardization of some data communication task across disciplines, or a joint visualization tool or joint education materials.
The guiding question: “Do you know some topic where astro- and geo-community could collaborate to produce new knowledge?”

OBJECTIVE 3: Defragmentation of Existing Knowledge

Short description: look at a bigger picture of bridging these (astro- and geo-informatics) disciplines and use international collaboration to defragment and systematize the Big Data knowledge they create. Experts from each discipline will provide their perspective on the importance of certain knowledge, which then can be put into the appropriate context. For example, visualization and image recognitions have a long history in computer science, including the gaming industry, but astronomers and Earth observers have a highly specialized knowledge how to identify objects in their imaging databases based on the specifics of science behind it (e.g. spectral, spatial and morphological properties of stars and galaxies or Earth surface categories).
The guiding question: “Can you identify a common set of asto- and geo-informatics tools that students and early stage researchers should learn to ease their crossing the disciplines? “

OBJECTIVE 4: Dissemination

Short description: reaching a larger audience and spreading the acquired knowledge. Objectives 1-3 will create a significant amount of material that will be useful to the larger community of experts in academia and business. However, a considerable effort will be invested into adapting this material to different levels of expertise. For example, some target groups, such as early stage researchers or experts from different fields, need not only help with numerical tools, but also some help with the underlying science or a statistical method.
The guiding question: “What should be done to help students and early stage researchers get interested in adopting key knowledge needed for successful work in astro- and geo-informatics? “

Working Groups

WG1: Optimisation of database tools in astro- and geophysics contexts

Short description: this group is focused more on the back-end tools providing support for knowledge extraction from large datasets (database management systems, hardware configurations, heterogeneous environments, location of processing of large users’ data-requests, etc).

WG2: Data mining and machine learning in the petabyte era as frontiers in astronomy and Earth observation

Short description: this group is inclined more toward front-end solutions visible to the users, where often tools used to date fail or are too slow on petabyte datasets.

WG3: Education of a new generation of experts in knowledge extraction from massive datasets

Short description: this group has the task of identifying critical gaps in the Big Data users’ skills, organizing materials needed for training and education of users, and organizing training sessions.

WG4: Visualization of high dimensional data

Short description: this group explores various aspects of visualization of large datasets under scientific and outreach requirements, promotes the role of visualization in data-mining (visual analytics) and assesses various visualization tools.