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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. Site. He is the Founder of the IEEE International Symposium on High Performance Distributed Computing (HPDC) and the cofounder of the IEEE International Conference on Autonomic Computing. His current research focuses on autonomic computing, high performance distributed computing, design and analysis of high speed networks, benchmarking and evaluating parallel and distributed systems, developing software design tools for high performance computing and communication systems, and network-centric applications. He is co-author/editor of four books on parallel and distributed computing: Autonomic

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Current social-network-based and location-based-service applications need to handle continuous spatial approximate keyword queries over geo-textual streaming data of high density. The continuous query is a well-known expensive operation. The optimization of continuous query processing is still an open issue. For geo-textual streaming data, the performance issue is more serious since both location information and textual description need to be matched for each incoming streaming data tuple. The state-of-the-art continuous spatial-keyword query indexing approaches generally lack both support for approximate keyword matching and high-performance processing for geo-textual streaming data. Aiming to tackle this problem, this paper first proposes an indexing approach for efficient supporting of continuous spatial approximate keyword queries by integrating m i n - w i s e signatures into an AP-tree, namely AP-tree + . AP-tree + utilizes the one-permutation m i n - w i s e hashing method to achieve a much lower signature maintenance costs compared with the traditional m i n - w i s e hashing method because it only employs one hashing function instead of dozens. Towards providing a more efficient indexing approach, this paper has explored the feasibility of parallelizing AP-tree + by employing a Graphic Processing Unit (GPU). We mapped the AP-tree + data structure into the GPU’s memory with a variety of one-dimensional arrays to form the GPU-aided AP-tree + . Furthermore, a m i n - w i s e parallel hashing algorithm with a scheme of data parallel and a GPU-CPU data communication method based on a four-stage pipeline way have been used to optimize the performance of the GPU-aided AP-tree + . The experimental results indicate that (1) AP-tree + can reduce the space cost by about 11% compared with MHR-tree, (2) AP-tree + can hold a comparable recall and 5.64× query performance gain compared with MHR-tree while saving 41.66% maintenance cost on average, (3) the GPU-aided AP-tree + can attain an average speedup of 5.76× compared to AP-tree + , and (4) the GPU-CPU data communication scheme can further improve the query performance of the GPU-aided AP-tree + by 39.4%.

Online trajectory compression is an important method of efficiently managing massive volumes of trajectory streaming data. Current online trajectory methods generally do not preserve direction information and lack high computing performance for the fast compression. Aiming to solve these problems, this paper first proposed an online direction-preserving simplification method for trajectory streaming data, online DPTS by modifying an offline direction-preserving trajectory simplification (DPTS) method. We further proposed an optimized version of online DPTS called online DPTS + by employing a data structure called bound quadrant system (BQS) to reduce the compression time of online DPTS. To provide a more efficient solution to reduce compression time, this paper explored the feasibility of using contemporary general-purpose computing on a graphics processing unit (GPU). The GPU-aided approach paralleled the major computing part of online DPTS + that is the SP-theo algorithm. The results show that by maintaining a comparable compression error and compression rate, (1) the online DPTS outperform offline DPTS with up to 21% compression time, (2) the compression time of online DPTS + algorithm is 3.95 times faster

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