**Abstract**

With the rise of complex systems and devices equipped with sensors that generate exponential data within seconds, most organizations still use methods and frameworks designed for static or historical data warehouses and therefore lack the capability to harness such high-frequency data streams on time. Effective management of time-oriented data requires much more work to be completed particularly if one needs to discern any special temporal relationships in data that may exist in space (region) and quantify how those relationships could impact other spaces (regions). A fusion of time and space (spatial temporal) data dimensions in knowledge systems can enable the discovery of untapped information that can be central to tackling many open research questions in vast domains. This chapter first, describes a collection of spatial-temporal knowledge management and sharing methods from the literature highlighting existing shortcomings where systems designed lacks capabilities to effectively harness data critical for making data-driven decisions on time. To address some of these challenges, an overarching spatial-temporal knowledge processing framework named Sesat is introduced. This new framework outlines principles adopted for designing effective spatial-temporal knowledge systems that can be effectively managed. A theoretical use case scenario within cyber security is demonstrated utilizing the Sesat framework thus highlighting the potential for such effective spatial-temporal knowledge management in many data domains.

**Keywords:** knowledge frameworks, spatial-temporal knowledge, data-driven intelligence, knowledge systems, knowledge management, expert systems
