A Light-Weight and Quality-Aware Online Adaptive Sampling Approach for Streaming Social Sensing in Cloud Computing
Yang Zhang, Daniel Zhang, Nathan Vance, Qi Li, Dong Wang · 2018
Social Sensing has emerged as a new distributed application paradigm in networked sensing where streaming data is constantly collected from humans or devices on their behalf. A key challenge in social sensing is that the applications are often both data-intensive and delay-sensitive, which often require extensive resources in the cloud to support the data processing and analytics tasks. To address this problem, this paper focuses on a quality-aware online adaptive sampling problem where the goal is to judiciously sample a small set of representative sensing measurements (i.e., representative set) that effectively represent the key information of the social sensing data stream. However, two technical challenges exist in identifying such a representative set: (i) “quality-aware quantification”: data collected in the social sensing applications is often unstructured (e.g., text, image, video) where the key information is so deeply embedded in the raw data that can not be easily quantified to ensure the desirable quality of the selected representative set; (ii) “online adaptive sampling”: the sampling decision has to be made in real-time and be adaptive to the dynamics of the streaming data in social sensing. To address these challenges, this paper develops a Light-weight and Quality-aware Online Adaptive Sampling (LQOAS) scheme to dynamically identify the representative set of measurements from the streaming social sensing data using a submodular maximization approach. We evaluate the LQOAS scheme using a real world social sensing dataset from Twitter. The evaluation results show that the LQOAS scheme significantly outperforms the state-of-the-art sampling schemes.