Support for the higher levels of reasoning requires that various supporting information must be produced. The lowest level components will perform functionality involving detection, discrimination and preliminary identification. Example components might map pixel level features to abstract model elements or filter time series data to detect significant events.
The nature of the bottom layer is to provide the basic interface to any information not derived by the framework itself. Although some simple forms of data may be directly mapped into the knowledgebase by processing elements in this layer, in many situations some additional analysis will be required. This analysis corresponds to the boundary between observations and inferred entities illustrated in Process Stack illustration . Rather that simply storing a direct representation of the "raw" data values, relevant abstract features are detected and identified, and placed in the knowledgebase as an instance of a class in the problem domain ontology. For example, rather than attempting to persist all the pixels in a reconnaissance image, relevant features are extracted and only the feature descriptions are captured. Of course this does not preclude any system external to the framework from persisting the raw data and archival data is also a valid source of information for processing by elements in the framework. But a general goal for each element in the framework is to progressively increase the usability of the knowledge present in the data.
It is particularly important to note that for each layer of the model, many different types and instances of processing can (and should) be operating in parallel. Rather than having a single "very smart" detection element that is configured to find many different kinds of features. Many smaller, tightly focused components should be employed, each one looking only for a specific feature. In fact it would be desirable in many cases to have multiple processing elements all looking for the same feature, but using different techniques, approaches, or technologies. It would be up to the higher layers of the model to fuse or reconcile the results.
Types of uncertainty present for this layer of the model are primarily data related as opposed to being more abstract or model related. Missing or noisy input data are two of the most common problems.
Bottom layer | Middle Layer | Top layer