The role of incentive-based crowd-driven data collection in big data analytics: A perspective

Abstract

Big data analytics for effective decision-making entails significant amounts of data collection. Existing sensor-based data collection mechanisms are expensive to deploy due to the high initial fixed costs of installing large-scale sensor-based systems. Sensors also require maintenance, thereby further adding to the costs. Moreover, sensors cannot be cost-effectively installed at all possible locations. Furthermore, some data collection scenarios require human judgment, which sensors are not capable of providing. To address the limitations associated with sensor-based data collection mechanisms, this paper discusses the role of incentive-based crowd-driven data collection in big data analytics. Given the increasing prevalence and popularity of mobile devices coupled with the fact that mobile devices often come equipped with various kinds of sensors, crowd-driven data collection is well-aligned with current technological trends. We also provide some directions about the kind of analytics that can be done on the crowd-collected data in case of different application scenarios. Furthermore, we discuss some of the open research issues in this area. © Springer International Publishing Switzerland 2013.