Emerging intelligent big data analytics for cloud and edge computing

Abstract

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence, and it exploits how to use artificial intelligence to enhance big data analytics for various applications.1 As cloud computing cannot meet the strict computing time requirement in latency-critical big data analysis applications, edge computing has emerged as a solution to address the drawbacks of cloud-based solutions by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex intelligent big data analytics tasks. Consequently, this special issue is focused on exploiting key techniques of intelligent big data analytics by involving cloud and edge computing. Presented with an avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and requires more studies to mine and integrate cloud-based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. For this reason, Chen et al2 proposed APEX2S, a novel two-layer machine learning model, for discovery of the protein-protein interactions data. APEX2S calibrated the focus for host-pathogen protein-protein interactions study, aiming to apply machine learning techniques for learning the interactions data and making predictions. To date, there are a wide variety of applications of human action recognition, such as surveillance, robotics, health care, video searching, and human-computer interaction. However, there are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. To solve this, Zhao et al3 proposed a novel action recognition method to improve the recognition accuracy by adopting the key frame extraction and multi-feature fusion techniques. A key frame extraction method based on node contribution weighting is proposed to extract video key frames, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Many applications are now deployed on Virtual Machines (VMs) or even Spot VMs elastically rented from public Clouds. To save costs, interval-priced VMs are not released until the ends of rented intervals. Such delays of control effects make existing methods rent or release excess VMs leading to over controls. Fluctuating prices make Spot VMs unreliable due to unexpected termination which makes fault-tolerant strategies crucial. In order to decrease the VM rental cost while guaranteeing the SLA and robustness, Cai et al4 proposed a hybrid control method UCM which takes advantage of queuing-model-based loosely coupled controllers, unequal-interval-based collaborating method, and an existing group-based fault tolerant strategy. Lidar-based city objects detection is an interesting topic along with the development of Laser scan equipment which has been widely applied in various applications such as 3D building reconstruction, navigation, and so on. Superpixel segmentations are widely applied to image processing or computer vision tasks. Many experiments have proven that superpixels generated from atomic meaningful pixel regions, can improve the processing efficiency while losing little information of the original image. Therefore, Mao et al5 describes a city object detection algorithm for airborne Lidar images using superpixel segmentation and DenseNet classification. A three-block DenseNet is applied to classify the superpixels into four main types of city objects (Building, road, field, and railway). In addition, a graph based neighborhood adjustment algorithm is designed to further improve the classification results. Virtual network embedding (VNE) aims to solve how t