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 $min-wise$ signatures into an AP-tree, namely AP-tree ${}^{+}$ . AP-tree ${}^{+}$ utilizes the one-permutation $min-wise$ hashing method to achieve a much lower signature maintenance costs compared with the traditional $min-wise$ 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 $min-wise$ 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%.

The Mongolian Plateau (MP), situated in the transitional zone between the Siberian taiga and the arid grasslands of Central Asia, plays a significant role as an Ecological Barrier (EB) with crucial implications for ecological and resource security in Northeast Asia. EB is a vast concept and a complex issue related to many aspects such as water, land, air, vegetation, animals, and people, et al. It is very difficult to understand the whole of EB without a comprehensive perspective, that traditional diverse studies cannot cover. Big data and artificial intelligence (AI) have enabled a shift in the research paradigm. Faced with these requirements, this study identified issues in the construction of EB on MP from a big data perspective. This includes the issues, progress, and future recommendations for EB construction-related studies using big data and AI. Current issues cover the status of theoretical studies, technical bottlenecks, and insufficient synergistic analyses related to EB construction. Research progress introduces advances in scientific research driven by big data in three key areas of MP: natural resources, the ecological environment, and sustainable development. For the future development of EB construction on MP, it is recommended to utilize big data and intelligent computing technologies, integrate extensive regional data resources, develop precise algorithms and automated tools, and construct a big data collaborative innovation platform. This study aims to call for more attention to big data and AI applications in EB studies, thereby supporting the achievement of sustainable development goals in the MP and enhancing the research paradigm transforming in the fields of resources and the environment.

Identifying ecologically vulnerable areas is critical for constructing ecological barriers and precisely controlling ecological risks. With the rapid development of big data and Artificial Intelligence (AI) technologies, many intelligent methods have been developed to support the identification of vulnerable ecological areas. This paper reviews the methodological advancements in identifying ecologically vulnerable areas, including geographic zoning, expert integration, mathematical statistics, geographic information visualization, artificial neural networks, and unsupervised deep learning clustering methods. Additionally, we assessed several classic software tools used in ecology and natural resource management. Based on the review, several urgent research challenges for ecological function zoning research are proposed, such as the application of ecological vulnerability assessment intelligent algorithms, big data collaborative analysis, and the development of automated identification software. Considering the requirements in the Mongolian Plateau, this study proposes future development prospects of methods for identifying ecologically vulnerable area zoning, combined with the new AI research paradigm. They include enhancing the comprehensive analysis of multimodal data, increasing ecological barrier big data collaborative processing, advancing the interpretability of ecological function partitioning algorithms, developing automatic zoning software tools, and pushing the collaborative analysis of geographic big data and citizen science data.