Topic
Crowdsourcing
About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.
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TL;DR: The potential of crowdsourcing as a tool for data analysis to address the increasing problems faced by companies in trying to deal with “Big Data” is considered.
90 citations
TL;DR: The results of four case studies support the assertion that the proposed crowdsourceable framework to quantify the Quality of Experience of multimedia content can provide reliable QoE evaluation at a lower cost.
Abstract: Crowdsourcing has emerged in recent years as a potential strategy to enlist the general public to solve a wide variety of tasks. With the advent of ubiquitous Internet access, it is now feasible to ask an Internet crowd to conduct QoE (Quality of Experience) experiments on their personal computers in their own residences rather than in a laboratory. The considerable size of the Internet crowd allows researchers to crowdsource their experiments to a more diverse set of participant pool at a relatively low economic cost. However, as participants carry out experiments without supervision, the uncertainty of the quality of their experiment results is a challenging problem. In this paper, we propose a crowdsourceable framework to quantify the QoE of multimedia content. To overcome the aforementioned quality problem, we employ a paired comparison method in our framework. The advantages of our framework are: 1) trustworthiness due to the support for cheat detection; 2) a simpler rating procedure than that of the commonly-used but more difficult mean opinion score (MOS), which places less burden on participants; 3) economic feasibility since reliable QoE measures can be acquired with less effort compared with MOS; and 4) generalizability across a variety of multimedia content. We demonstrate the effectiveness and efficiency of the proposed framework by a comparison with MOS. Moreover, the results of four case studies support our assertion that the framework can provide reliable QoE evaluation at a lower cost.
90 citations
TL;DR: The ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and the problems and challenges that need further research are investigated.
Abstract: In the last decade, crowdsourcing has emerged as a novel mechanism for accomplishing temporal and spatial critical tasks in transportation with the collective intelligence of individuals and organizations. This paper presents a timely literature review of crowdsourcing and its applications in intelligent transportation systems (ITS). We investigate the ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and identify the problems and challenges that need further research. Finally, we briefly introduce our future works focusing on using geospatial tagged data to analyze real-time traffic conditions and the management of traffic flow in urban environment. This review aims to help ITS practitioners and researchers build a state-of-the-art understanding of crowdsourcing in ITS, as well as to call for more research on the application of crowdsourcing in transportation systems.
90 citations
TL;DR: In this paper, a new generation of big data analytics (BDA) companies are crowdsourcing large volumes of online consumer reviews by means of controlled ad hoc online experiments and advanced machine learning (ML) techniques to forecast demand and determine the market potential for new products in several industries.
90 citations
09 May 2017
TL;DR: This tutorial gives an overview of crowdsourcing, and then summarizes the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data management.
Abstract: Many important data management and analytics tasks cannot be completely addressed by automated processes. Crowdsourcing is an effective way to harness human cognitive abilities to process these computer-hard tasks, such as entity resolution, sentiment analysis, and image recognition. Crowdsourced data management has been extensively studied in research and industry recently. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowdsourced data management. We first give an overview of crowdsourcing, and then summarize the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data management. Next we review crowdsourced operators, including selection, collection, join, top-k, sort, categorize, aggregation, skyline, planning, schema matching, mining and spatial crowdsourcing. We also discuss crowdsourcing optimization techniques and systems. Finally, we provide the emerging challenges.
90 citations