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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Ge, Ningchao Chen, Haiwen Wu, Jibing Hu, Ying National University of Defense Technology Laboratory for Big Data and Decision Changsha China Criminal Investigation Section Public Security Department Changsha China

ISBN: (纸本)9798350330076

With the in-depth development of big data and artificial intelligence technology, traditional financial, transportation, manufacturing, military and other industries are gradually developing in the direction of digitalization, intelligence and diversification of service scenarios. The transformation of data level is from single-domain data demand to multi-domain data demand. However, the traditional single-domain data management methods are difficult to meet the needs of large-scale multi-domain data management. Aiming at the characteristics of independent autonomy, complex structure and difficult aggregation that are common in distributed heterogeneous environments. This paper puts forward a multi-domain data agile management framework: multi-federated distributed management framework (Multi-FDMF). Based on the idea of "star atom structure"and "federal management institution", the framework realizes the internal federal management of all kinds of data in the field by constructing a local distributed federal system in each "atom"layer. Some "atoms"are linked to the "molecular"center through data channels to form a multi-domain "molecular"external federated system. Finally, multi-federated distributed management of multi-domain data is realized through multiple links at the "molecular"level and above. © 2023 IEEE.

关键词： Information management

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Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study [Extended Abstract] 40

Matching Knowledge Graphs in Entity Embedding Spaces: An Exp...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zeng, Weixin Zhao, Xiang Tan, Zhen Tang, Jiuyang Cheng, Xueqi National University of Defense Technology Laboratory for Big Data and Decision China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China Institute of Computing Technology CAS China

ISBN: (纸本)9798350317152

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches KGs in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development. © 2024 IEEE.

关键词： Embeddings

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Metabolomics Combined with Network Pharmacology Uncovers Effective Targets of Tao-Hong-Si-Wu Decoction for Its Protection from Sepsis-Associated Acute Lung Injury

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Journal of Analysis and Testing 2023年第2期7卷 172-186页

作者： Jinzhi He Longfei Wang Yang Wang Zeng Li Feihu Chen Zhicheng Liu Anhui Provincial Laboratory of Infl ammatory and Immunity Disease Anhui Institute of Innovative Drugs School of PharmacyAnhui Medical UniversityHefei 230032China Shanxi Key Laboratory of Big Data for Clinical Decision Research School of ManagementShanxi Medical UniversityTaiyuan 030000China

Sepsis-induced acute lung injury(ALI)is a leading cause of death among septic ***-Hong-Si-Wu decoction(TSD),a classical recipe from traditional Chinese medicine used for treating ischemic stroke,has been recently reported to alleviate inflammation and inflammation-stimulated injuries related to the pathology of ***,we first observed the therapeutic effect of TSD on sepsis-induced *** on integrated metabolomics and network pharmacology analysis(NPA)techniques,we aim to understand the mechanism of TSD alleviating ***’s effects were observed in rats modeled by cecal ligation and puncture(CLP)and rat macrophages stimulated by lipopolysaccharide(LPS).Metabolomics analyses were applied to determine the ingredients in the medicine and key metabolites correlated to the NPA for the prediction of TSD *** and protein expressions of the key predicted targets were evaluated in the lung tissue and macrophages of septic model rat by quantitative polymerase chain reaction(PCR)and enzyme-linked immunosorbent assays,*** improved survival rate and protected against lung injury in CLP *** endogenous metabolites were related to TSD’s *** significantly suppressed IL-6 and TNF-αsecretions and their gene expressions both in the lung tissue of the model rats and in LPS-stimulated *** also restored decreased lung protein expression of VEGFA in septic model *** proteins and their affecting metabolites were finally validated in an external test set of *** study shows that metabolomics coupled with NPA is a promising approach to explore potential targets of medicine with complex compositions.

关键词： Metabolomics Drug eff ect analysis Sepsis Traditional Chinese medicine Cytokines Bioanalysis

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STIOS: A Novel Self-supervised Diffusion Model for Trajectory Imputation in Open Environment Scenarios

STIOS: A Novel Self-supervised Diffusion Model for Trajector...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhijing Hu Hao Yan Yuhao Zheng Hua He Chao Chen Changjun Fan Kuihua Huang Laboratory for Big Data Data and Decision National University of Defense Technology Changsha China School of Computer Science and Engineering Central South University Changsha China Laboratory for Big Data Data and Decision National University of Defense Technology China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

With the growth of location-based services, the accumulation of large amounts of trajectory data comes with the challenge of missing data. Existing trajectory imputation methods rely on deterministic models that cannot take into account open environments scenarios without auxiliary information. This may lead to poor performance in learning the long-term dependencies of trajectories. In this paper, we propose a novel Self-supervised diffusion model for Trajectory Imputation in Open environment Scenarios (STIOS), which combines a deterministic model and a conditional diffusion model. In addition, we propose a self-supervised training method inspired by masked language modelling that divides the data into conditional information and estimation targets. Extensive experiments on two real datasets demonstrate the superiority of STIOS for trajectory imputation.

关键词： Training Social computing Uncertainty Computational modeling Roads Estimation Diffusion models Imputation data models Trajectory

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IGFClust: Clustering Unbalanced and Complex Single-Cell Expression data by Iteration and Integrating Gini Index and Fano Factor 17th

IGFClust: Clustering Unbalanced and Complex Single-Cell Exp...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Li, Han Zeng, Feng Yang, Fan Department of Automation Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen China

ISBN: (纸本)9789819924424

With ScRNAseq, we are able to obtain genome-wide transcriptome data from single cells. However, it is very difficult to identify all cell subpopulations in single cell expression data, especially when these subpopulations are unbalanced and the number of subpopulations is unknown. In this paper, we propose a new clustering algorithm, IGFClust. We design an ensemble method to identify unbalanced subpopulations using Gini index and Fano factor. In addition, we design an iterative clustering framework to avoid the problem that only some subpopulations can be identified during the clustering process. We generated four sets of labeled simulation data and compared IGFClust with existing methods. Afterwards, we analyzed 576 glioblastoma primary tumor cells. We show that IGFClust performs accurately and robustly in identifying complex and unbalanced single-cell expression data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Iterative methods

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Causal Analysis of Bone Disease Risk Factors Based on Markov Boundary Discovery 9

Causal Analysis of Bone Disease Risk Factors Based on Markov...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Liu, Lihua Wang, Haoran Zhu, Youdian Ge, Ningchao Hu, Shengze National University of Defense Technology Laboratory for Big Data and Decision Changsha China National University of Defense Technology College of Systems Engineering Changsha China

ISBN: (纸本)9798350330076

Bone diseases, affecting millions globally, necessitate a nuanced understanding of their underlying causal mechanisms for effective prevention and treatment. Traditional machine learning and deep learning methods, while powerful in identifying patterns and associations, often fall short in distinguishing correlation from causation. This paper addresses this gap by employing the Markov boundary discovery algorithm, a novel causal inference technique, to analyze risk factors associated with bone diseases. Utilizing a comprehensive dataset of patient information, including the occurrence of bone diseases and related factors, we identify the direct causes of bone disease presence, providing a clear and concise depiction of the causal relationships at play. Our results demonstrate a strong alignment with expert opinions, validating the efficacy of our approach and its potential to revolutionize bone disease research. By bridging the gap between data-driven insights and clinical expertise, this study contributes to the enhanced understanding of bone disease etiology, the development of targeted interventions, and the improvement of patient outcomes, marking a significant stride in the field of biomedical research. © 2023 IEEE.

关键词： Factor analysis

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Research Landscapes, Hotspots and Emerging Frontiers of Combinatorial Optimization Algorithms on Graphs: A Scientometric Analysis (1987 - 2022) 9

Research Landscapes, Hotspots and Emerging Frontiers of Comb...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Sun, Boliang Chen, Hai You, Ziqi National University of Defense Technology Laboratory for Big Data and Decision ChangSha China National University of Defense Technology College of System Engineering ChangSha China

ISBN: (纸本)9798350330076

This paper presents a scientometric analysis of the research landscapes, hotspots, and emerging frontiers of combinatorial optimization algorithms on graphs from 1987 to 2022. The study investigates technical maturity, country/region collaboration, institutional cooperation, journal co-citation, dual graph overlay, and hotspot frontier analysis based on co-citation and keyword co-occurrence using tools like CiteSpace, Bibliometrics R, and VOSviewer. The analysis reveals that research in this field has entered a mature stage, and countries such as the United States, China, and Japan have produced rich output in this field. The University of Tokyo, MIT, the Chinese Academy of Sciences, Shanghai Jiao Tong University, Stanford University, and the University of California have formed 10 distinct collaborative clusters. The findings also suggest that research in the field of combination optimization mainly comes from two clusters, namely, computer science and mathematics, and that cross-disciplinary knowledge is also involved. In the field of graph-based combination optimization, deep learning, machine learning, graph neural networks, and reinforcement learning are now areas of intense investigation. The frontier direction of research in this field is represented by quantum computing technologies such as coherent ising machines, hybrid quantum-classical algorithms, and quantum algorithms. The research problems in this field are categorized into three main types from a keyword perspective: graph theory problems, classical optimization problems, and open problems. The corresponding methods for solving these problems are also categorized into combinatorial optimization techniques, stochastic optimization methods, and machine learning-based optimization methods. The study highlights the need for interdisciplinary collaboration, more research on quantum computing technologies, advanced optimization techniques, and the exploration of emerging research frontiers in combination o

关键词： Graph theory

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Trajectory Planning for Spacecraft Far-field Rendezvous via Convex Optimization 9

Trajectory Planning for Spacecraft Far-field Rendezvous via ...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Jiang, Wenzheng Yin, Zeyang Wang, Ji Zhang, Xiongtao Chen, Wenting Jia, Zhiqiang National University of Defense Technology Laboratory for Big Data and Decision Changsha410073 China South University School of Automation Central Changsha410083 China

ISBN: (纸本)9798350330076

Safe rendezvous operations of spacecraft require the capability to generate highly constrained trajectories timely. For far-field rendezvous operation, the trajectory planning of the spacecraft is a complex problem due to factors such as arbitrary orbits and the unignorable mass changes caused by fuel consumption. A trajectory planning method is proposed in this paper which considering low engine thrust and representative constraints such as approach corridors, relative speeds, etc. Utilizing various convexification techniques, a corresponding relaxed version of the original problem is formulated. Next, an innovative iterative solution methodology is introduced for addressing the relaxed problem. After discretization, the original nonlinear problem is restructured into a sequence of second-order cone programming problems, whose solutions can be obtained through convex optimization algorithms. Simulation experiments have provided evidence that this approach is effective, confirming its reliability and potential for practical application. © 2023 IEEE.

关键词： Iterative methods

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Research on Multi-UAV Path Planning and Modeling for Ship Search 9

Research on Multi-UAV Path Planning and Modeling for Ship Se...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Jia, Boyu Li, Zhimeng Zhang, Chengming Bao, Yizheng Lin, Xiang Yuan, Yutong National University of Defense Technology Laboratory for Big Data and Decision Changsha China National University of Defense Technology College of Systems Engineering Changsha China

ISBN: (纸本)9798350330076

A set of modeling method and optimization model for multi-UAV cooperative target searching at sea is proposed. First, a basic model is built according to the maneuvering rules of ships and drones. Then, on the basis of considering the load balance of each UAV, the historical discovery probability of sea area is used to update the target probability map information. Finally, the ant colony algorithm is used to optimize the search route of each UAV, considering both the energy supply demand of UAV and the maximum target discovery probability. The simulation results show that the proposed method has high search efficiency and can be effectively applied to the fast search task of Marine targets, which has important application value. © 2023 IEEE.

关键词： Drones

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A Brief Survey of Vector databases 9

A Brief Survey of Vector Databases

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Xie, Xingrui Liu, Han Hou, Wenzhe Huang, Hongbin National University of Defense Technology College of Systems Engineering Changsha China National University of Defense Technology Laboratory for Big Data and Decision Changsha China

ISBN: (纸本)9798350330076

The explosive growth of massive high-dimensional data requires capabilities for data processing, storing, and analyzing. This brings significant challenges to traditional databases due to the poor ability to handle high-dimensional data and its original design for stand-alone machines. Fortunately, vector databases have provided a practical solution for the management and analysis of high-dimensional data. Especially, they retrieve results related to the query efficiently after encoding various forms of data (e.g., text, image, and video) into vectors. The purpose of this paper is to offer insight into vector databases by presenting a brief survey. Firstly, the workflow of vector databases including indexing and querying, is detailed along with a specific case. Subsequently, we elaborate on the related methods applied in vector databases, which are the core techniques to enhance search efficiency and reduce computational overhead, particularly similarity search algorithms and similarity metrics. Further, we introduce widely used vector database products (e.g., Pinecone, Chroma, and Milvus) and compare them from multiple factors that should be taken into consideration. We also discuss potential avenues for future research in this domain. To conclude, this survey provides a comprehensive understanding of vector databases for retrieval from vast high-dimensional datasets. © 2023 IEEE.

关键词： Vectors

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