Database management systems (or DBMSs) have been around for decades, and yet are still difficult to use, particularly when trying to identify and fix errors in user programs (or queries). We seek to understand what methods have been proposed to help people debug database queries, and whether these techniques have ultimately been adopted by DBMSs (and users). We conducted an interdisciplinary review of 112 papers and tools from the database, visualisation and HCI communities. To better understand whether academic and industry approaches are meeting the needs of users, we interviewed 20 database users (and some designers), and found surprising results. In particular, there seems to be a wide gulf between users' debugging strategies and the functionality implemented in existing DBMSs, as well as proposed in the literature. In response, we propose new design guidelines to help system designers to build features that more closely match users debugging strategies.

We explore the reduction of a fuzzy classifier designed to perform a binary classification of tracked or wheeled vehicles based on acoustic data. A genetic algorithm is used to explore the design space of the classifier, with variations performed on the number of antecedents included in the final fuzzy system. Besides the original individual set generated by the GA, we define a subset of it with a small number of antecedents as a filtered set. A novel method of extracting important system components, known as open product analysis, is applied to these two sets, yielding systems that perform well with a small number of antecedents. The fuzzy classifier we reduced performs well using only 20 to 30% of the antecedents that were originally used for classification.

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