**5. Data science workflows**

Recently, a new paradigm for Earth observation, namely, Data Knowledge Discovery, was introduced [17]. This paradigm defines the entire chain "datainformation-knowledge-value" and deals with a meaningful EO content extraction, i.e., the semantic and knowledge aspects.

We developed user-invariant and EO domain-specific compensatory methods for the individual user- and domain-subjective biases. The derived models generate a sharable knowledge body as a means to enable the communication between fragmented knowledge learned from metadata, image data, and other data in synergy with the domain expertise of EO users. Today's EO paradigms and technologies are largely domain-oriented and have to support the communication outlined above.

Artificial intelligence big data in Earth observation [13] forced the development of new technologies starting from management platforms [4] and is reaching now the information platforms.

An example for the first category are ESA's Thematic Exploitation Platforms (TEPs) [4] that are designed and focused for coastal applications, forest, geohazards, hydrology, polar, urban, and food and security application domains, integrating standard processing chains that have low user interaction. The Copernicus system (currently still under development) and its data information and access services component [18] are a major achievement but still represent a "classic" management paradigm.

Currently, "classic" existing systems/platforms are usually batch-oriented (e.g., TEPs, DIAS), but with EOLib [20, 40] and the new CANDELA platform [17], this paradigm was "moved" to interactive systems (e.g., supporting active learning).

There are three perspectives to describe this type of interactive systems:
