Josep Lluís Arcos

Case-Based Reasoning (CBR) methodology’s approach to problem-solving that “similar problems have similar solutions” has proved quite favorable for many industrial artificial intelligence applications. However, CBR’s very advantages hinder its performance as case bases (CBs) grow larger than moderate sizes. Searching similar cases is expensive. This handicap often makes CBR less appealing for today’s ubiquitous data environments while, actually, there is ever more reason to benefit from this effective methodology. Accordingly, CBR community’s traditional approach of controlling CB growth to maintain performance is shifting towards finding new ways to deal with abundant data.
As a contribution to these efforts, this thesis aims to speed up CBR by leveraging both problem and solution spaces in large-scale CBs that are composed of temporally related cases, as in the example of electronic health records. For the occasions when the speed-up we achieve for exact results may still not be feasible, we endow the CBR system with anytime algorithm capabilities to provide approximate results with confidence upon interruption. Exploiting the temporality of cases allows us to reach superior gains in execution time for CBs of millions of cases. Experiments with publicly available real-world datasets encourage the continued use of CBR in domains where it historically excels like healthcare; and this time, not suffering from, but enjoying big data.