Anticipatory Analytics

Overview This paper outlines how anticipatory analytics might lead to active and real time knowledge representation and exchange. This is a theoretical paper, with potential for designing, building and deploying new social media equipped with knowledge generation, representation and transfer tools. Topic representation is in the form of the topic map standard. Topics are made objects of discussion within a private communication medium. The architecture is illustrated as supporting medical informatics, supply chain analytics and analytics in education. The paradigm provides measurable knowledge transfer between individuals within social media. Various modeling tools are used to develop a knowledge representation base consisting of enumerated topics. Results from group or corporate analytics are associated with topics. Knowledge representation management uses systems analysis to describe various processes occurring within a company, for example the acquisition of data, training, the use of materials in manufacturing or production, supply chain analysis, employment procedures, client relationships, or how information is generated and used. Services are expressed using only topics represented in the knowledge base. In all cases, root causes of typical events may be revealed. Methodology directed at identifying root causes find commonalities between different events or processes are represented as topics. Feature extraction is applied to the full spectrum of events and processes. The knowledge space is considered to be full only if any event or process may be properly described using a subset of the topics enumerated as the knowledge space. The representation of enumerated topics forms a digital representation of all event and processes, including event and process deep structure. For example, supply chain analysis may be couched using terms derived from various forms of analysis. Finding terms that refer to multi-use components are indicators that those components play an essential role in supply to manufacturing and other forms of production. As more of these multi-use components are identified a capacity to discuss root causes is created. Root causes will express using event or process components having multiple purposes and in repeated parts of the activities of an enterprise. Empirical studies guide implementation. Representation of deep structure may be enhanced in various ways. Clustering of topics produce an index over how topic representations become associated as knowledge transfer is achieved and as discussions evolve. For example, an observed aggregation might suggest natural co-occurrences between specific topics and predicted process expression patterns based on pathways over a graph structure. In a form of crowd …