**4. Spatial-temporal knowledge management framework in the phase of big data**

In this section, a spatial-temporal knowledge management framework named Sesat is introduced. Sesat name is adopted from Seshat, the Egyptian goddess of

knowledge, wisdom, and writing, and was seen as a record keeper and illuminator [36]. In recognition that for spatial-temporal knowledge to be effectively harnessed, there has to be end-to-end processing of space and time data from multiple sources to application efficiently. To this respect, Sesat is a framework designed as a multi-layered process flow with agents that continuously enable data ingestion from multiple sources, processing to sharing and application. To accomplish this, Sesat multi-agents are designed with tasks for accomplishing many complex tasks simultaneously as presented pictorially in **Figure 1** and described next.

There are seven layers within Sesat as follows:


*A Spatial-Temporal Knowledge Management Framework DOI: http://dx.doi.org/10.5772/intechopen.101797*

### **Figure 1.**

*Spatial-temporal knowledge management framework (Sesat).*

The multiple agents adopted in Sesat address many challenges in existing knowledge frameworks that are still designed from a static data mindset. We argue that by incorporating streamed lined layers of data flow and agents, such a framework if adopted in spatial-temporal knowledge creation, management and sharing provides a unified end-to-end process flow on real-time knowledge management applicable to any data domain.

Next, we demonstrate a theoretical application of Sesat to a cyber-network domain with a use case scenario.
