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Data exploration has received much attention during the last few years. The aim is to learn interesting new facts from a possibly unfamiliar data set. Typically, explorers operate by trial and error: they write a query, inspect the results and refine their specifications accordingly. In this demo proposal, we present Ziggy, a system to help them understand their query results. Ziggy's aim is to complement an existing exploration system. It assumes that users already have a query in mind, but they do not know what is interesting about it. To assist them, it detects characteristic views, that is, small sets of columns on which the tuples in the results are different from those in the rest of the database. Thanks to these views, our explorers can understand why their selection is unique and make more informed exploration decisions.

Blaeu is an interactive database exploration tool. Its aim is to guide casual users through large data tables, ultimately triggering insights and serendipity. To do so, it relies on a double cluster analysis mechanism. It clusters the data vertically: it detects themes, groups of mutually dependent columns that highlight one aspect of the data. Then it clusters the data horizontally. For each theme, it produces a data map, an interactive visualization of the clusters in the table. The data mapssummarize the data. They provide a visual synopsis of the clusters, as well as facilities to inspect their content and annotate them. But they also let the users navigate further. Our explorers can change the active set of columns or drill down into the clusters to refine their selection. Our prototype is fully operational, ready to deliver insights from complex databases.