Paper Info
Title
Enhanced query processing for NoSQL crowdsourcing systems
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.
Year
DOI
Venue
2014
10.1109/SOCPAR.2014.7008049
Soft Computing and Pattern Recognition
Keywords
Field
DocType
natural language processing,query processing,IMDb dataset,NoSQL crowdsourcing systems,NoSQL database system,Web sites,crowd opinions,enhanced query processing,natural language sentences,product review collections,social knowledge
Data structure,Information retrieval,Ranking,Computer science,Crowdsourcing,Search engine indexing,Exploit,NoSQL,Natural language,Semantics
Conference
Citations 
PageRank 
References 
0
0.34
11
Authors
4
Name
Order
Citations
PageRank
Alfredo Cuzzocrea11751200.90
Marcello Di Stefano200.34
Paolo Fosci343.54
Giuseppe Psaila4722192.45