ﻻ يوجد ملخص باللغة العربية
The scientific method drives improvements in public health, but a strategy of obstructionism has impeded scientists from gathering even a minimal amount of information to address Americas gun violence epidemic. We argue that in spite of a lack of federal investment, large amounts of publicly available data offer scientists an opportunity to measure a range of firearm-related behaviors. Given the diversity of available data - including news coverage, social media, web forums, online advertisements, and Internet searches (to name a few) - there are ample opportunities for scientists to study everything from trends in particular types of gun violence to gun-related behaviors (such as purchases and safety practices) to public understanding of and sentiment towards various gun violence reduction measures. Science has been sidelined in the gun violence debate for too long. Scientists must tap the big media data stream and help resolve this crisis.
Big medical data poses great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists and engineers sit together to discuss several fundamental issues. F
Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage res
Life pattern clustering is essential for abstracting the groups characteristics of daily mobility patterns and activity regularity. Based on millions of GPS records, this paper proposed a framework on the life pattern clustering which can efficiently
Big, fine-grained enterprise registration data that includes time and location information enables us to quantitatively analyze, visualize, and understand the patterns of industries at multiple scales across time and space. However, data quality issu
Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their non-representativeness and the bias of statistical inferenc