A cognitive being is capable of understanding data, and exploiting it. Element Cognitive Hub is tasked with understanding data, and performing knowledgeable actions based on it.
Whereas the Sensory Hub is a real-time state machine, the Cognitive Hub is a near-real-time demon, continuously monitoring ingested information in order to enrich it and provide productive insights from it. The Hub is fully automatic and is focused on proactively retrieving information and searching for missing information, based on preconfigured rules.
Element Sensory interprets data inlights of configurable ONTOLOGIES, defined by domain experts, to map the organization knowledge using Semantic (Neural) Networks. These Ontologies capture the various Entities, Topics, Relationships, Domain-Specific-Lingo, and other aspects of organization data perception.
Unlike conventional data processing, which can only handle neatly organized structured data, Element applies artificial intelligence to understand unstructured data which is the predominant data available today, and at the same time are harder to consume.
The Cognitive Hub doesn't just perform search engine like, key word matching, but really interprets text like a person. Relying on Natural Language Processing tools, and domain specific Ontologies, Element can read data written for humans, in its context. It does so by using grammatical rules, semantic correlation, and sentiment interpretation of texts.
All ingested data continues flowing through configurable processes, that iteratively enrich the data in context. Available data is retrieved using federated querying from internally and externally mapped repositories, and where Element identifies gaps in data, it triggers searches to complete it (e.g. seek person address from a high credential source, while identifying its current location from social media).
In parallel, the data is persisted in the system hybrid data layer, having factual information arranged in Knowledge Graphs, raw data maintained and indexed in big data repositories, transactional data maintained in a data-warehouse, etc.
As the knowledge graph evolves, Element applies analysis tools to further exploit the collected data. Geospatial and Temporal analytics and Behavioral Science are applied to identify patterns and anomalities in data, understand their implications and act upon them. Entity Analytics are applied on the ingested information to perform Entity Resolution and Disambiguation, names resolution, Relationship Resolution, etc., Predictive Analytics is applied to device policies for mitigating risks, and acting on opportunities, and at the same time, Element seeks to enrich the governing Ontologies, to assist in the organization learning process.
The Cognitive Hub continues to apply logic based policies on the information, and where it judges human intervention is required, based on the maturity of information, its relevance, its impact, etc., it can also escalate it to the internal or external data consumer, deemed relevant, by pushing it to the Discovery Hub.