TAIM AND DISCOVERY COMPUTATION FROM WAREHOUSED DATA

The morphing Transformer Active Intelligent Database designs outline in the chapter is abbreviated as TAIM henceforth. Data discovery from KR on diagrams might be viewed as satisfying a goal by getting at relevant data which instantiates a goal. The goal formula states what relevant data is sought. We have presented planning techniques, which can be applied to implement discovery planning. In planning with G-diagrams that part of the plan that involves free Skolemized trees is carried along with the proof tree for a plan goal. The idea is that if the free proof tree is constructed then the plan has a model in which the goals are satisfied. The model is the initial model of the AI world for which the free Skolemized trees were constructed. Partial deductions in this approach correspond to proof trees that have free Skolemized trees in their representation. While doing proofs with free Skolemized trees we are facing proofs of the form p(g(....)) proves p(f(g(....)) and generalizations to p(f(x)) proves for all x, p(f(x)). Thus the free proofs are in some sense an abstract counterpart of the SLD. Let us see what predictive diagrams do for knowledge discovery knowledge management. Diagrams allow us to model-theoretically characterize incomplete KR. To key into the incomplete knowledge base

Figure 10.1 depicts selector functions Fi from an abstract view grid interfaced via an inference engine to a knowledge base and in turn onto a database.

Generalized predictive diagrams are defined, whereby specified diagram functions and search engine can select onto localized data fields. A Generalized Predictive Diagram is a predictive diagram where D (M) is defined from a minimal set of functions. The predictive diagram could be minimally represented by a set of functions {f1, ..., fn} that inductively define the model. The functions are keyed onto the inference and knowledge base to select via the areas keyed to, designated as Si’s in Figure 10.1 and data is retrieved (Nourani, 1999f). Visual object views to active databases might be designed with the above. The trees defined by

Keyed KR, inference, and visual discovery

FIGURE 10.1 Keyed KR, inference, and visual discovery.

the notion of provability implied by the definition might consist of some extra Skolem functions {g1, ..gn}, that appear at free trees. The f terms and g terms, tree congruences, and predictive diagrams then characterize deduction with virtual trees (Nourani, 1999b) as intelligent predictive interfaces. Data discovery from KR on diagrams might be viewed as satisfying a goal by getting at relevant data which instantiates a goal. The goal formula states what relevant data is sought. We have presented planning techniques, which can be applied to implement discovery planning. In planning with G-diagrams that part of the plan that involves free Skolemized trees is carried along with the proof tree for a plan goal. The idea is that if the free proof tree is constructed then the plan has a model in which the goals are satisfied. The model is the initial model of the AI world for which the free Skolemized trees were constructed. Partial deductions in this approach correspond to proof trees that have free Skolemized trees in their representation. While doing proofs with free Skolemized trees we are facing proofs of the form p(g(...)) proves p(f(g(...)) and generalizations to p(f(x)) proves for all x, p(f(x)). Thus the free proofs are in some sense an abstract counterpart of the SLD. Practical AI Goal Satisfaction. The predictive diagram could be minimally represented by a set of functions {f1, ..., fan} that inductively define the model. The free trees we had defined by the notion of provability implied by the definition, could consist of some extra Skolem functions {g1, ..., gl} that appear at free trees. The f terms and g terms, tree congruences, and predictive diagrams then characterize partial deduction with free trees.

 
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