Architecture Lifecycle Integrity of Reactive Metadata Models in association to the Cognitive Contextualization

Recent advancements in Big Data computing indicates that contextualization provisions interpretation and communicative acts of any deployable intelligent constitution of the autonomous system, process, product, and service. A large amount of research in pragmatics has proved the wide-ranging and multifaceted characteristics of the cognitive contextualization. Traditionally, the contexts are formulated and leveraged in terms of Metadata. Deriving cognitive metadata and their interrelationships are challenging since it is extremely complex to understand underlying intellectual knowledge of advances in Big Data computations of a real-time enterprise (RTE). Different participants of RTE have different goals, drivers, and experiences. They are affected by the different types of information necessary to perform or operate RTE’s processes. According to different experiences, different structurized cognitive context factors come into being. All of these factors will influence the RTE of the discourse. We are introducing concept of reactive metadata model (RMM) management. It leverages reactive architecture to develop metadata models in anticipation of cognitive contexts. Primary focus is to streamline RMM and their utilization through the distinct architecture lifecycle stages across growing connected ecosystems of RTE. The approach achieves transparency in the heterogeneity of knowledge and intelligence across the RTE. It proactively constitutes desired dynamism of cognitive contextualization in the RMM. We illustrate introduction of consistency, accuracy, and extendibility due to publishing of cognitive context aware RMM in particles of application programming interfaces (APIs).