Current spatial-keyword publish/subscribe systems need to handle spatial-keyword skyline queries over geo-textual streams to continuously obtain good results. The skyline queries in such systems face two main problems: (1) query problems, because the powerful query capability is required for the strict limit of the response time and the large number of items concerned by the users, and (2) scalability issue, because millions of active users are maintained simultaneously with many network-connected machines. Unfortunately, the current approach is towards static data. Thus, this paper first proposes a distributed skyline query processing framework. Then, we optimize the skyline computing by introducing MF-R$^t$-tree, which is an update-efficient and space-saving indexing structure and a fast approach for processing a continuous spatial-keyword skyline query called$eager^*$. Finally, a spatial and textual signature-based communication optimization method is proposed to support scalability. The experimental results indicate that (1) MF-R$^t$-tree can significantly reduce update costs, while maintaining a low storage cost, and a query performance comparable to IL-Quadtree, (2)$eager^*$can averagely accelerate 79.72 × faster than the method based on BNL, (3) the communication optimization method significantly reduces the communication cost, and (4) the distributed framework can efficiently support large-scale skyline queries.

Object detection, aiming to recognize and locate objects of interest in aerial images, has historically played a significant role in the remote sensing community. Following remarkable improvements in Earth observation technologies, high-resolution remote sensing (HRRS) images with a bird’s eye view perspective have revealed many categories of objects with sufficient variations in appearance and on complex backgrounds that make HRRS object detection an active but challenging task. The selection of positive samples and negative training instances is an essential factor in influencing detectors’ performance. Related studies have found that many low-quality negative samples in the detectors’ training process have caused training instability and low detection accuracy. In this work, a novel sampling-balance-based multistage network (SB-MSN) is presented to adaptively mine high-quality positive and negative instances for training an accurate detector. It has a series of components to ensure the selection and generation of high-quality examples for training an accurate detector, including a multiscale information retention module, an intersection over union balance sampling strategy, a balance L1 loss, and a multistage network. The proposed detector has been evaluated on three representative HRRS data sets. The extensive experimental results show that our detector can solve the problem of low-quality samples and significantly improve the detection performance of the mAP by 1.4% with the NWPU VHR-10 data set, 3.5% with the high-resolution remote sensing detection (HRRSD) data set, and 4.2% with the detection in the optical remote (DIOR) data set.1

In real simulation applications, simulations often involve large volumes of three-dimensinal (3D) moving objects. With the rapid growth of the scale of simulation-problem domains, it has become a key requirement to efficiently manage massive 3D moving objects. Conventional indexing approaches for managing 3D moving objects during simulations generally sufferfrom excessive update costs. Aiming to this problem, this paper first proposes an update-efficient indexing structure by fusing a loose Octree and one update-memo structure, namely ML-Octree. ML-Octree significantly reduces the update costs of one simulation involving massive 3D moving objects. Towards providing a more efficient indexing approach, this paper has explored the feasibility of paralleling ML-Octree by employing Graphic Processing Unit (GPU). A load-balancing scheme is used to further improve the update performance of the GPU-aided ML-Octree. Finally, a distributed GPU-aided ML-Octree is proposed for large-scale simulations. The experimental results indicate that (1) ML-Octree can acquire the update-performance gain of an order of magnitude similar to that of Octree, (2) the GPU-aided ML-Octree can accelerate 5.07χ fasterthan a parallel ML-Octree with 8 CPU threads on average, (3) the load-balance scheme can improve GPU-aided ML-Octree by 2.3χ on average, and (4) the distributed GPU-aided ML-Octree can efficiently support large-scale simulations.

Summary PM2.5 time series have the features of non‐stationary and nonlinear. Existing forecasting methods for PM2.5 cannot achieve high accuracy for they have ignored the potential characteristics of PM2.5 time series. Aiming at this problem, a hybrid approach using local mean decomposition and Support Vector Regression (SVR)‐Elman (LSE) is firstly proposed in this paper to analyse 5days ahead PM2.5 concentrations for forecasting in Wuhan, China: (1) the meaningful PF1‐PF5 components are extracted from original PM2.5 time series by local mean decomposition; (2) the first high‐frequency product function is managed by using the SVR model, such that the relationship between PM2.5 and other air quality data can be revealed accurately; (3) the other components are trained by Elman model with the sliding window method. Experimental results show that, compared with multiple linear regression, autoregressive integrated moving average, BP neural network, and SVR models, the proposed hybrid LSE model‐based approach exhibits the best performance in terms of R2, MAE, MAPE, RMSE, while it is applied for forecasting in real datasets. Copyright © 2016 John Wiley & Sons, Ltd.

Simulation study on evacuation scenarios has gained tremendous attention in recent years. Two major research challenges remain along this direction: (1) how to portray the effect of individuals' adaptive behaviors under various situations in the evacuation procedures and (2) how to simulate complex evacuation scenarios involving huge crowds at the individual level due to the ultrahigh complexity of these scenarios. In this study, a simulation framework for general evacuation scenarios has been developed. Each individual in the scenario is modeled as an adaptable and autonomous agent driven by a weight-based decision-making mechanism. The simulation is intended to characterize the individuals' adaptable behaviors, the interactions among individuals, among small groups of individuals, and between the individuals and the environment. To handle the second challenge, this study adopts GPGPU to sustain massively parallel modeling and simulation of an evacuation scenario. An efficient scheme has been proposed to minimize the overhead to access the global system state of the simulation process maintained by the GPU platform. The simulation results indicate that the “adaptability” in individual behaviors has a significant influence on the evacuation procedure. The experimental results also exhibit the proposed approach's capability to sustain complex scenarios involving a huge crowd consisting of tens of thousands of individuals.

More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.

Range query in Peer-to-Peer networks based on Distributed Hash Table (DHT) is still an open problem. The traditional way uses order-preserving hashing functions to create value indexes that are placed and stored on the corresponding peers to support range query. The way, however, suffers from high index maintenance costs. To avoid the issue, a scalable blind search method over DHTs - recursive partition search (RPS) can be used. But, RPS still easily incurs high network overhead as network size grows. Thus, in this paper, a learning-aware RPS (LARPS) is proposed to overcome the disadvantages of two approaches above mentioned. Extensive experiments show LARPS is a scalable and robust approach for range query, especially in the following cases: (a) query range is wide, (b) the requested resources follow Zipf distribution, and (c) the number of required resources is small.

The storage management initiative specification (SMI-S) has been proposed for years to standardize the management of storage resources in storage area network (SAN). However, current management architecture mainly focuses on local area management. In this paper, we propose a scalable management architecture based on the integration of SMI-S and Chord. Meanwhile, Chord can only deal with exact query, the query of storage resources is significantly more complex. To deal with this problem, we further improve our architecture with a scalable blind search method - recursive partition search (RPS). Experiments show RPS is an effective approach for storage resource range query in the case that the Chord overlay network is not very large.