CrowdMiner
Yael Amsterdamer, Yael S. Grossman, Tova Milo, Pierre Senellart · Proceedings of the VLDB Endowment · 2013
This demo presents CrowdMiner, a system enabling the mining of interesting data patterns from the crowd. While traditional data mining techniques have been used extensively for finding patterns in classic databases, they are not always suitable for the crowd, mainly because humans tend to remember only simple trends and summaries rather than exact details. To address this, CrowdMiner employs a novel crowd-mining algorithm, designed specifically for this context. The algorithm iteratively chooses appropriate questions to ask the crowd, while aiming to maximize the knowledge gain at each step. We demonstrate CrowdMiner through a Well-Being portal, constructed interactively by mining the crowd, and in particular the conference participants, for common health related practices and trends.