Crowdsourced Data Managementt: Overview and Challenges

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.