In this paper, we provide a novel approach for effectively and efficiently
support query processing tasks in novel NoSQL crowdsourcing systems. The idea of
our method is to exploit the social knowledge available from reviews about products of
any kind, freely provided by customers through specialized web sites. We thus define
a NoSQL database system for large collections of product reviews, where queries can
be expressed in terms of natural language sentences whose answers are modeled as
lists of products ranked based on the relevance of reviews w.r.t. the natural language
sentences. The best ranked products in the result list can be seen as the best hints for
the user based on crowd opinions (the reviews). By exploiting the well-known IMDb
dataset, which comprises more than 2 million reviews for more than 100,000 movies,
we experimentally shows that our prototype obtains good performance in terms of
execution time, demonstrating that our approach is feasible.
