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.cordingly. In this demo proposal, we present Ziggy, a system t Typically, explorers operate by trial and error: they write a query, inspect the results and refine their specifications aco 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.

Additional Metadata
Persistent URL dx.doi.org/10.14778/3007263.3007287
Project The SciLens-II Infrastructure, Big Data at work , Commit: Time Trails (P019)
Conference International Conference on Very Large Data Bases
Grant This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/621.016.201 - The Scilens-II Infrastructure, Big Data at work
Citation
Sellam, T.H.J, & Kersten, M.L. (2016). Ziggy: Characterizing query results for data explorers. In Proceedings of the VLDB Endowment (pp. 1473–1476). doi:10.14778/3007263.3007287