## 3 Discussion and Proposed Solution
In the context of rapid sensemaking within a social networking environment, a lexicon of \"textspeak\" linked to a thesaurus would prove most useful as would algorithms for modifying the ratings of entities within a discourse model. An integrated module could provide information and movement on profiles via geo-tagged data. Dynamic global supply chains and the increased geographic responsibilities of decision support systems (DSS) have underscored the need to quickly analyze large datasets so as to adequately engage in the realm of rapid sensemaking (querying, analyzing, etcetera) for security-related processing. While many enterprise systems were previously principally architected and optimized for performance, next-generation intelligent engineering system approaches necessitate architecting and engineering, robust security focussed systems to be evolutionarily resilient and defensible in real time. There is currently a paradigm shift in dataset/computation amalgams -- the utilization of the quicker computational intelligence technique of moving the computation to the data instead of the traditional methodology of moving the data to computation is driving a need for real-time High Performance Computing. The processing speed advantage of such systems, on the order of several magnitudes, immediately alters the outcomes of numerous cyber security cost-benefit analysis (CBA) models; the prior return on investment (ROI) considerations and management inclinations that certain cyber security technologies are preliminarily obviated because they may hinder productivity are now being reversed. Thus an entire spectrum of cyber defence approaches predicated upon this newly validated corpus of viable defence-in-depth contributions come into play creating new opportunities for real-time architectures supporting real-time services such as the contextualisation and rapid sense-making as defined within SNeSSI. In many of these core analytics cases, real-world applications require algorithms that can return within minutes rather than hours; intrinsically, they rely upon the aforementioned sensemaking to locate the pertinent relevance over very large data volumes in quasi-real-time. Furthermore, with every new observation from the myriad of artificial/human sensors, these sensemaking algorithms, which take advantage of this operational tempo, can very readily be configured for the conjoining of diverse data within the same data space and discriminating relationships for expressive context accumulation (ECA), whilst distinguishing noise (e.g., re-tweets) for robust semantic reconciliation (RSR). In the context of SNeSSI this means real-time metadata and keyword analysis and processing with real-time feedback via an avatar as depicted in Figure 2.  
The Graphical User Interface with its intuitive design and an animated avatar ensures users' interest in SNeSSI. A real-time inference engine, the linchpin of the architecture, controls the GUI and is informed by an extensive knowledge base comprising of CERS engine, geospatial knowledge, ontology and an archive of known security issues. The inference engine is responsible for making an informed decision about the risk factor associated with the user's interaction with SNS and enrichesthe knowledge base. The inference engine also feeds into the Gateway component which acts as the communication bridge between SNeSSI and various commercial SNS.