Evolutionary Intelligence and Quality-Of-Information: A Specific Case Modelling

Evolutionary Intelligence and Quality-Of-Information: A Specific Case Modelling

Eliana Pereira (University of Minho, Portugal), Eva Silva (University of Minho, Portugal), Bruno Fernandes (University of Minho, Portugal) and José Neves (University of Minho, Portugal)
DOI: 10.4018/978-1-4666-9882-6.ch015
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The strategy of making predictions for a specific case or problem, in particular regarding scenarios with incomplete information, should follow a dynamic and formal model. This chapter presents a specific case concerning the employment of professionals for a health institution, as technicians and physicians, to demonstrate a model that requires the Quality-of-Information and the Degree-of-Confidence of the extensions of the predicates that model the universe of discourse. It is also mentioned a virtual intellect, or computational model, in order to maximize the Degree-of-Confidence that is associated with each term in the extensions of the predicates, according to the approximate representation of the universe of discourse. This model is prepared to be adopted by a Business Intelligence platform in order to increase the Quality-of-Information and the Degree-of-Confidence of the extensions in healthcare.
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BI systems are rapidly being adopted to provide enhanced analytical capabilities to previously existing systems that manage and integrate a very large array of information. BI systems are defined as specialized tools for data analysis, query, and reporting that support organizational decision-making that potentially enhances the performance of a range of business processes. BI systems are also complemented by specialized IT infrastructure (including Data Warehouses, Datamarts and Extract Transform and Load (ETL) tools) which is necessary for their deployment and effective use (Elbashir, Collier, & Davern, 2008).

In order to improve the QoI comes the implementation of BI strategies. This is a major step since it provides actionable information delivered at the right time, at the right location, and in the right form to assist the decision-making process. By improving the timeliness and quality of inputs, only valuable information is considered and the final models are the most accurate possible (Negash, 2004).

Some non-classical techniques to model the universe of discourse and reasoning procedures of intelligent systems have been proposed and many of them based on logic with probability theory (Kakas, Kowalski, & Toni, 1998) (Pereira & Lopes, 2007) (Subrahmanian, 2001).

Logic programming has emerged as an attractive formalism for Knowledge Representation and Reasoning tasks, introducing an efficient mechanism for solving search problems (Neves, et al., 2012).

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