Big Data最新文献

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine; however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers' dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider's services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention.

Recognition of handwritten Uchen Tibetan characters input has been considered an efficient way of acquiring mass data in the digital era. However, it still faces considerable challenges due to seriously touching letters and various morphological features of identical characters. Thus, deeper neural networks are required to achieve decent recognition accuracy, making an efficient, lightweight model design important to balance the inevitable trade-off between accuracy and latency. To reduce the learnable parameters of the network as much as possible and maintain acceptable accuracy, we introduce an efficient model named HUTNet based on the internal relationship between floating-point operations per second (FLOPs) and Memory Access Cost. The proposed network achieves a ResNet-18-level accuracy of 96.86%, with only a tenth of the parameters. The subsequent pruning and knowledge distillation strategies were applied to further reduce the inference latency of the model. Experiments on the test set (Handwritten Uchen Tibetan Data set by Wang [HUTDW]) containing 562 classes of 42,068 samples show that the compressed model achieves a 96.83% accuracy while maintaining lower FLOPs and fewer parameters. To verify the effectiveness of HUTNet, we tested it on the Chinese Handwriting Data sets Handwriting Database 1.1 (HWDB1.1), in which HUTNet achieved an accuracy of 97.24%, higher than that of ResNet-18 and ResNet-34. In general, we conduct extensive experiments on resource and accuracy trade-offs and show a stronger performance compared with other famous models on HUTDW and HWDB1.1. It also unlocks the critical bottleneck for handwritten Uchen Tibetan recognition on low-power computing devices.