Article
Computer Science, Artificial Intelligence
Taizhi Lv, Lian Tong, Jun Zhang, Yong Chen
Summary: The proposed method is a real-time physiological signal acquisition and analysis method based on fractional calculus and stream computing. It estimates physiological status using a fractal index, calculated by distributed parallel computing on a stream computing platform. Experimental results show its ability to distinguish heart health status and reflect driver mental status.
Article
Computer Science, Cybernetics
Diego Cordeiro Barboza, Debora C. Muchaluat-Saade, Esteban Walter Gonzales Clua, Diego Gimenez Passos
Summary: This study introduces a novel approach based on cloud computing that optimizes encoding and streaming of 2D game content by splitting it into multiple layers, significantly reducing processing time and stream bitrates. By storing a local client-side cache and updating data with small packets, the method achieves minimal user-perceived delay or impact on video quality.
ENTERTAINMENT COMPUTING
(2022)
Article
Computer Science, Theory & Methods
Hongjian Li, Hongxi Dai, Zengyan Liu, Hao Fu, Yang Zou
Summary: Two energy-efficient scheduling algorithms are proposed in this study to reduce energy consumption for streaming applications in Storm. By integrating tasks into low energy consumption nodes and optimizing load balance, the total energy consumption of Storm cluster has been effectively reduced.
Article
Computer Science, Information Systems
Jueon Park, Kyungyong Lee
Summary: In this paper, we propose a model called S-MPEC for predicting and optimizing the latency of sparse matrix multiplication (SPMM) tasks in distributed cloud environments using Apache Spark. By characterizing different distributed SPMM implementation methods and considering the characteristics and hardware specifications of the cloud, we establish an accurate prediction model that recommends the optimal implementation method. The experimental results show that users can expect a 44% reduction in latency compared to native SPMM implementations in Apache Spark.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Nhu-Ngoc Dao, Anh-Tien Tran, Ngo Hoang Tu, Tran Thien Thanh, Vo Nguyen Quoc Bao, Sungrae Cho
Summary: This article provides a contemporary survey of cutting-edge live video streaming studies from a computation-driven perspective. It presents an overview of global standards, system architectures, and streaming protocols. Hierarchical computation-driven models of live video streaming, including cloud-, edge-, and peer-to-peer-based solutions, are explored. The article reviews cutting-edge studies that have improved system performance in various areas and presents open challenges for future research in the field.
ACM COMPUTING SURVEYS
(2022)
Article
Computer Science, Information Systems
Fan Gao, Peng Yue, Zhipeng Cao, Shuaifeng Zhao, Boyi Shangguan, Liangcun Jiang, Lei Hu, Zhe Fang, Zheheng Liang
Summary: This paper proposes a new geospatial infrastructure called GeoCube, which expands the capacity of data cubes to handle multi-source big vector and raster data, thereby facilitating the management and analysis of big Earth observation data. The paper also highlights key research contributions in online analytical processing.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Tsozen Yeh, Yulin Chen
Summary: This paper discusses how to accelerate job execution speed in hybrid cloud environments by optimizing the data transfer process. By designing and implementing a new model, the execution time of jobs was reduced significantly, improving data access efficiency and having a positive impact on cloud computing environments.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
Article
Computer Science, Information Systems
Jongbeen Han, Hyeonsang Eom, Yongseok Son
Summary: This article proposes an automatic and cost-effective VM scheduling framework for managing VMs in streaming services. By applying a content-based VM scheduling policy, it enables lower costs and performance overheads.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Yongjun Ren, Ding Huang, Wenhai Wang, Xiaofeng Yu
Summary: This paper designs a blockchain-based secure storage mechanism (BSMD) to address the storage issue of massive data. The mechanism adopts an on-chain and off-chain cooperative storage model and utilizes updatable subvector commitment for secure authentication protocol, ensuring consistency of on-chain and off-chain data, and batch processing capability. The correctness and security of the proposed protocol are proven, and the performance is analyzed.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Information Systems
Jeong-Hoon Kim, Sun-Hyun Kim, Charn-Doh Bak, Seung-Jae Han
Summary: This paper addresses the challenge of achieving cost-efficiency and user satisfaction in global-scale multimedia live streaming services through cloud-based adaptive resource allocation. By introducing a cloud-based multi-tier architecture called MaaS, and utilizing a combination of deep-learning and dynamic programming, the proposed scheme successfully balances user experience and cloud resource cost, outperforming existing schemes.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Anup Mohan, Ahmed S. Kaseb, Yung-Hsiang Lu, Thomas J. Hacker
Summary: This paper introduces a method to select cloud instances to meet the performance requirements for visual data analysis at a lower cost. By measuring frame rates when analyzing data using different computer vision methods and modeling the relationships between frame rates and resource utilizations, the problem of managing cloud resources is formulated as a Variable Size Bin Packing Problem and solved using a heuristic solution. Experiments using Amazon EC2 validate the model and show that the proposed solution can reduce costs by up to 62 percent while meeting performance requirements.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2021)
Article
Computer Science, Information Systems
Hong Liu, Xiaojing Lu, Shengchen Duan, Yushu Zhang, Yong Xiang
Summary: This paper presents a scheme for efficient and oblivious access to encrypted databases through encrypted indexes. The scheme utilizes semi-homomorphic encryption to perform calculations in the ciphertext domain, reducing communication and storage overhead significantly. It achieves high-security encrypted search and update operations, with execution speed 2-8x faster than ORAM-based schemes, and reduces data block transmission and storage costs compared to existing frameworks.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Mathematics
Elham Azhir, Mehdi Hosseinzadeh, Faheem Khan, Amir Mosavi
Summary: Access plan recommendation is a query optimization approach that utilizes prior created query execution plans to execute new queries. This study uses Apache Spark and Apache Hadoop frameworks in a MapReduce-based method to cluster query datasets of different sizes and evaluates their performance. The results demonstrate the effectiveness of parallel query clustering in achieving high scalability, with Apache Spark outperforming Apache Hadoop with an average speedup of 2x.
Article
Computer Science, Information Systems
Chunlin Li, Mingyang Song, Chongchong Yu, Youlong Luo
Summary: The cooperative edge-cloud computing architecture is effective in reducing the burden on streaming media service systems, with streaming content caching being crucial. This paper proposes cache and content placement strategies based on user mobility and content popularity, resulting in significant improvement in user service quality.
INFORMATION SCIENCES
(2021)
Article
Engineering, Industrial
Moritz von Stietencron, Karl Hribernik, Katerina Lepenioti, Alexandros Bousdekis, Marco Lewandowski, Dimitris Apostolou, Gregoris Mentzas
Summary: Logistics 4.0 focuses on sustainable customer satisfaction and cost optimization using emerging technologies like IoT and streaming analytics. This paper introduces a software framework for streaming analytics in an edge-cloud environment to advance Logistics 4.0, with a specific application and evaluation in the aerospace industry.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Agronomy
Shuzhe Huang, Xiang Zhang, Nengcheng Chen, Hongliang Ma, Jiangyuan Zeng, Peng Fu, Won-Ho Nam, Dev Niyogi
Summary: The proposed downscaling framework successfully integrates deep learning, triple collocation method, and point-surface fusion technique to downscale the original SSM data to 1 km spatial resolution, which has been validated in Southwestern US, demonstrating high accuracy and advantages.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Wenying Du, Yue Gong, NengCheng Chen
Summary: In this study, an integrated method based on particle swarm optimization and weakly labeled support vector machine was proposed to assess urban waterlogging susceptibility. The proposed model was tested in different districts of Wuhan, China under various rainstorm scenarios, and demonstrated better performance compared to other methods. The model can accurately identify high waterlogging susceptibility areas and help decision-making for urban waterlogging control.
COMPUTERS & GEOSCIENCES
(2022)
Article
Environmental Sciences
Shuzhe Huang, Xiang Zhang, Nengcheng Chen, Hongliang Ma, Peng Fu, Jianzhi Dong, Xihui Gu, Won-Ho Nam, Lei Xu, Gerhard Rab, Dev Niyogi
Summary: This paper proposes a novel SSM fusion framework to generate high-resolution, accurate, and seamless data for surface soil moisture estimation. By combining remote sensing, reanalysis, and in-situ data sets, the proposed method utilizes downsampling and deep learning models to improve the accuracy of SSM estimation. Validation results show that the proposed GRASPS method achieves good performance in SSM estimation, making it promising for fine-scale studies and applications in agricultural, hydrological, and environmental domains.
WATER RESOURCES RESEARCH
(2022)
Editorial Material
Environmental Sciences
Soheil Sabri, Abbas Rajabifard, Yiqun Chen, Nengcheng Chen, Hao Sheng
Article
Environmental Sciences
Peng Yang, Wenyu Wang, Xiaoyan Zhai, Jun Xia, Yulong Zhong, Xiangang Luo, Shengqing Zhang, Nengcheng Chen
Summary: This study investigates the impact of changes in terrestrial water storage on potential floods in the Yangtze River Basin. By reconstructing water storage data and analyzing flood potential index, the study reveals the significant influence of precipitation and water storage variations on flood risk.
Article
Environmental Sciences
Xiang Zhang, Tailai Huang, Aminjon Gulakhmadov, Yu Song, Xihui Gu, Jiangyuan Zeng, Shuzhe Huang, Won-Ho Nam, Nengcheng Chen, Dev Niyogi
Summary: In this study, a novel deep learning model was developed to fuse multi-source data and generate high-resolution and high-accuracy air temperature data. The model performed well in fitting the complex relationship between temperature and predictive variables and was validated in two urban regions, Wuhan and Austin. The results showed that the model achieved promising performance and can be used to generate valuable data for urban climate and urban heat island research.
Review
Environmental Sciences
Siqi Wang, Xiang Zhang, Nengcheng Chen, Liqiao Tian, Yan Zhang, Won-Ho Nam
Summary: The global expansion of cyanobacterial blooms poses a major risk to freshwater resources. This study investigated the driving forces and quantitative relationships of cyanobacterial blooms through a systematic literature review and statistical meta-analysis. The results showed that water quality, hydraulic conditions, meteorological conditions, and nutrient levels were identified as the primary driving forces of cyanobacterial blooms in global freshwater systems. The study also revealed negative or positive correlations between different driving forces and cyanobacterial blooms.
ENVIRONMENTAL RESEARCH
(2023)
Article
Environmental Sciences
Yan Zhang, Nengcheng Chen, Siqi Wang, Mengtian Wen, Zeqiang Chen
Summary: This study investigates the impact of carbon pricing policies on city GDP and found that it effectively reduces inequalities between rich and poor regions. The study also identifies spatial clustering patterns of carbon emissions and analyzes the main drivers of carbon emissions in different types of cities.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Environmental Sciences
Shitong Zhou, Lei Xu, Nengcheng Chen
Summary: Timely and accurate crop yield information is crucial for ensuring regional food security. Deep learning techniques like LSTM and CNN are commonly used for predicting crop yields. Studies have shown that combined models perform better than single models. However, using remote sensing data alone ignores the spatial heterogeneity of different regions.
Article
Environmental Sciences
Lei Xu, Hongchu Yu, Zeqiang Chen, Wenying Du, Nengcheng Chen, Chong Zhang
Summary: Ocean primary productivity is crucial for ocean ecosystems and the carbon cycle. Accurate forecasting of ocean primary productivity months in advance is beneficial for marine management. This study proposes a joint forecasting model that combines seasonal climate predictions and temporal memories of relevant factors to examine the predictability of ocean productivity. The results show that the combination of seasonal SST predictions and local memory can skillfully predict a large portion of productive oceans at different lead times. The hybrid data-driven and model-driven approach improves the predictability of ocean productivity, with seasonal climate predictions playing a significant role.
Article
Environmental Sciences
Shuzhe Huang, Yuan Gan, Xiang Zhang, Nengcheng Chen, Chao Wang, Xihui Gu, Jingjin Ma, Dev Niyogi
Summary: Urbanization globally modifies temperature and rainfall, leading to shifts from light to extreme rainfall and increased risk of floods and droughts. This study investigates the impact of urbanization on rainfall and drought events using statistical analysis and model simulations. The findings show that urbanization generally increases heavy rainfall and decreases light rainfall in rainy seasons. The study also reveals that urban regions are more susceptible to drought due to urbanization. However, the effects of urbanization on rainfall and drought vary across different regions due to complex terrains and climate patterns. The research provides valuable insights for urban planning and preparedness for urbanization-induced hazards.
Article
Computer Science, Information Systems
Wenying Du, Chang Ge, Shuang Yao, Nengcheng Chen, Lei Xu
Summary: This study conducted applicability analysis on BERT and four traditional methods for social media textual data and found their performance varies in different situations. The study also discovered the topic evolution pattern and hotspot changes related to a rainstorm in Henan. By integrating multiple methods, the overall topic classification accuracy of Sina microblogs was improved, facilitating spatiotemporal analysis of flooding.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2023)
Article
Environmental Sciences
Lei Xu, Hongchu Yu, Zeqiang Chen, Wenying Du, Nengcheng Chen, Min Huang
Summary: Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are important hydrological variables for agricultural water management and vegetation growth. The accuracy of SSM and RZSM prediction at sub-seasonal scales is valuable for water management in agriculture. Current weather model-based predictions of soil moisture have uncertainties, while data-driven machine learning methods lack consideration of soil water process for modeling long-term temporal dependence. Hence, we combine the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model with a data-driven stacked deep learning model to develop a hybrid SSM and RZSM forecasting framework.
Article
Engineering, Electrical & Electronic
Lei Xu, Jie Liu, Nengcheng Chen
Summary: This study explored the spatiotemporal variance of water quality in China's lakes and reservoirs during the COVID-19 pandemic based on the Forel-Ule index (FUI) retrieved from satellite imagery. The findings showed significant improvements in water quality post-epidemic, with different responses observed in eastern and western lakes.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Remote Sensing
Yan Zhang, Fan Zhang, Libo Fang, Nengcheng Chen
Summary: The study explores the relationship between street view images and point of interest data sources, proposing a Seq2Seq framework to integrate these two types of data. Results from experiments in Wuhan demonstrate the effectiveness of this method, enriching the way we perceive cities and aiding in the understanding of many-to-many relationships.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)