Article
Computer Science, Information Systems
Lara Lorna Jimenez, Olov Schelen
Summary: This article introduces HYDRA, a decentralized and distributed orchestrator for containerized microservice applications. HYDRA focuses on scalability and resiliency, enabling global manageability of cloud and edge environments. The experiments prove the viability of this orchestrator design.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Automation & Control Systems
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Chiara Verdone
Summary: This paper introduces a Data-aware Explainable Next Activity Prediction approach called DENAP based on the adoption of Long Short-Term Memory (LSTM) neural networks and Layer-Wise Relevance Propagation (LRP) method for the detection of next activity prediction in a business process and for the evaluation of the past activities and data that influence the prediction. The DENAP approach is validated on a set of synthetic and real logs. The obtained results show the good capability of DENAP to predict the next activity and indicate the more relevant activities/data with respect to the prediction.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhipeng Gui, Yunzeng Sun, Le Yang, Dehua Peng, Fa Li, Huayi Wu, Chi Guo, Wenfei Guo, Jianya Gong
Summary: The prediction of individual driving destinations is crucial for location-based services such as personalized service recommendations, traffic navigation, and public transport dispatching. Existing studies often overlook spatial factors, prompting the proposal of the LSI-LSTM model, which enhances prediction accuracy through location semantics extraction and trajectory spatial attention mechanism.
Article
Computer Science, Information Systems
Patryk Osypanka, Piotr Nawrocki
Summary: Cloud computing services are increasingly popular, but they can also lead to high operating costs. Many efforts have been made to optimize cloud resource usage and reduce expenses, but such optimization often comes at the expense of service responsiveness and quality, especially when dealing with real-world data and anomalies. This article proposes a novel approach that incorporates machine learning-based load prediction, discovery of service characteristics, long-term resource planning, anomaly detection, and continuous monitoring to achieve cost optimization without sacrificing performance. Evaluation using Microsoft's Azure cloud environment showed cost savings ranging from 31% to 89% depending on the test scenario.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Telecommunications
Paulo S. Souza, Tiago C. Ferreto, Fabio D. Rossi, Rodrigo N. Calheiros
Summary: This letter presents two maintenance strategies, Lamp and Laxus, which take into account user locations to make migration decisions during edge server maintenance, resulting in a 44.27% reduction in maintenance time compared to existing strategies while effectively avoiding delay bottlenecks.
IEEE COMMUNICATIONS LETTERS
(2022)
Article
Computer Science, Information Systems
Junwei Zhang, Yuqing Wang, Zhuo Ma, Xiaohan Yang, Zuobin Ying, Jianfeng Ma
Summary: With the rapid development of the Internet of Vehicles (IoV), location-aware outsourcing data aggregation plays a critical role in analyzing a large amount of data among smart devices. However, location privacy and data security face challenges in IoV due to the spatial property of data. In this article, we propose a location-aware verifiable outsourcing data aggregation (LAVODA) scheme for IoV, providing verifiability and data confidentiality using homomorphic encryption and commitment.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Wei Hua, Ziyang Zhou, Linyu Huang
Summary: With the development of 5G and IoT, cloud-based access mode is widely used in IoT scenarios. Mobile-edge computing (MEC) effectively improves execution efficiency and energy consumption. However, existing studies overlook the privacy and server load balancing issues caused by MEC.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Meng Chen, Qingjie Liu, Weiming Huang, Teng Zhang, Yixuan Zuo, Xiaohui Yu
Summary: Next location prediction is important for location-based applications. The existing methods neglect earlier passed locations in the trajectory. In this study, a Travel Time Difference Model is proposed to predict next locations by considering the travel time from all passed locations. Experimental results on real-world datasets demonstrate significant improvements in prediction accuracy over baseline methods.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, Bing Wang
Summary: Recent studies have shown that fusing GPS and WiFi data can predict depression more accurately. More complete data leads to stronger correlations and improves the accuracy of depression prediction.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Engineering, Civil
Mofeng Yang, Weiyu Luo, Mohammad Ashoori, Jina Mahmoudi, Chenfeng Xiong, Jiawei Lu, Guangchen Zhao, Saeed Saleh Namadi, Songhua Hu, Aliakbar Kabiri, Ya Ji
Summary: This paper presents a big-data driven framework that utilizes mobile device location data to estimate vehicle volume. By employing cloud computing and map matching algorithms, the vehicle trajectories are connected to the road network and weighted and calibrated to obtain reliable vehicle volume estimation.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Computer Science, Theory & Methods
Zhirun Zheng, Zhetao Li, Hongbo Jiang, Leo Yu Zhang, Dengbiao Tu
Summary: In this paper, a novel semantic-aware privacy-preserving online location trajectory sharing mechanism is proposed to protect both data privacy and semantic privacy while preserving data utility. Theoretical analysis proves the effectiveness of the mechanism, and experimental evaluations show its superiority over existing mechanisms.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Information Systems
Shahriar Badsha, Xun Yi, Ibrahim Khalil, Dongxi Liu, Surya Nepal, Elisa Bertino, Kwok Yan Lam
Summary: The personalized Web service recommendation based on Quality of Service (QoS) is gaining popularity, with collaborative filtering techniques contributing to high accuracy predictions. User location is also an important factor, and privacy-preserving protocols can ensure secure and practical recommendations without disclosing private information.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Engineering, Civil
Lei Tang, Zongtao Duan, Yishui Zhu, Junchi Ma, Zihang Liu
Summary: This paper aims to provide an optimal passenger matching solution by recommending ridesharing groups of passengers from GPS trajectories. Existing algorithms for rider grouping usually rely on matching pre-selected origin-destination coordinates, ignoring semantics in the spatial layout, while this approach can improve accuracy.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mario Luca Bernardi, Marta Cimitile, Fabrizio Maria Maggi
Summary: This study uses a multi-perspective declarative language to model the behavior of malware and trusted applications, and identifies malware applications and evaluates their membership to malware families through system call traces. The empirical study shows that the approach performs well in identifying infected applications and evaluating their family membership, and exhibits high performance and robustness against code transformations and evasion techniques.
Article
Computer Science, Information Systems
Yu Huang, Josh Jia-Ching Ying, Philip S. Yu, Vincent S. Tseng
Summary: Route planning satisfied multiple requests is an emerging branch in the field that has attracted significant attention. The proposed framework, MWMD-Router, comprehensively addresses the problem of Multi-weight Multi-destination Route Planning with Deadlines Constraints. This work is the first to consider handling multiple deadlines and optimizing multiple travel costs simultaneously.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)