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.

Querying NoSQL-based crowdsourcing systems efficiently

CUZZOCREA, Alfredo Massimiliano;
2016

Abstract

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.
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9788896354889
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2898344
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