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Hyperbit: A Financial Temporal Knowledge Graph Data Storage System

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SSRN

SSRN 2022年

作者： Yuan, Pingpeng Han, Sheng Zang, Shaoqi Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

In the field of finance, data has the characteristics of temporal multi-frequency and heterogeneous high dimension, and the traditional knowledge graph is not suitable to express the temporal relation of financial data, so the financial temporal knowledge graph has become a research hotspot recently. There are a large number of temporal edges in the financial temporal knowledge graph. The use of general graph database management system to store financial knowledge graph data has problems such as large storage scale and slow update speed, and it cannot realize the traceability of financial data, so it cannot meet the needs of the financial field. In order to solve these problems, HyperBit, a storage system for efficient storage and management of financial data, is designed and implemented. Firstly, a temporal quad storage structure is proposed. Secondly, the Log Structured Cumulative Update (LSCU) is proposed to update financial temporal data efficiently. The update log module is designed in the system. When there is data to update, it is not directly updated in the data storage of the system, but recorded in the update log, and the system updates the content of the update log cumulatively. The system manages update logs by partition and chunk, which makes the log storage compact and independent. Besides, it is convenient for parallel processing and it improves the data update speed. Thirdly, a Bidirectional Backtracking by Final and Optimal Root (BBFOR) method is proposed to meet the requirements of efficient traceability of financial data. Unlike traditional version tracing method, BBFOR adopt both the snapshot and incremental methods which balance the space and time cost of version switching, and BBROF consider the version of the access frequency characteristic and set up the final root and the optimal root. Besides, BBROF adopt bidirectional backtracking technology to improve the efficiency of the version switching. Experimental results show that the storage cost

关键词： Switching

Genome-wide identification and analysis of MAPK and MAPKK gene families in Brachypodium distachyon

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Genome-wide identification and analysis of MAPK and MAPKK ge...

第十四届全国植物基因组学大会

作者： Lihong Chen Yang Zhang Yuan He Wei Hu Shenglong Tan Min Wang Shiyi Zhou Xiaomin Deng Chao Huang Kexiu Li Junli Chang Mingjie Chen Yuesheng Wang Guangxiao Yang Guangyuan He The Genetic Engineering International Cooperation Base of Chinese Ministry of Science and Technology Key Laboratory of Molecular Biophysics of Chinese Ministry of EducationCollege of Life Science and TechnologyCollege of Life Science and TechnologyHuazhong University of Science & Technology(HUST) Services Computing Technology and System Laboratory Cluster and Grid Computing LaboratorySchool of Computer Science and TechnologyHuazhong University of Science & Technology(HUST)

Mitogen-activated protein kinase(MAPK) cascade,consisting of three Ser/Thr protein kinases,namely,MAPKKK,MAPKK and MAPK,is a universal module in signal transduction pathways in *** proteins have been implicated in diverse cellular processes including cell growth,proliferation,differentiation,survival, development and in responses to a diversity of environmental stimuli including cold, heat,reactive oxygen species,UV,drought and pathogen attack.

关键词： Brachypodium distachyon MAPK MAPKK phylogenetic analysis yeast two hybrid expression profile

A Distributed Workflow Management Approach in CGSP

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A Distributed Workflow Management Approach in CGSP

International Conference on grid and Cooperative computing (GCC)

作者： Haifeng Ou Xiaowu Chen Bin Cai Haijun Cao The Key Laboratory of Virtual Reality Technology Ministry of Education School of Computer Science and Engineering Beihang University Beijing China South China University of Technology Guangzhou China Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China

Workflow management technologies play a very important role in constructing grid application environment by automating service composition and execution. With the fast spread of grid technology, workflow management system should be flexible to meet increasing requirements of service composition. This paper introduces a distributed workflow management approach DWM4CGSP named distributed workflow management for CGSP, which can improve capability of workflow execution by dynamically assimilating new workflow engines and balancing workload among these engines. Moreover dynamic load balancing is applied in DWM4CGSP in order to make workload on each engine at reasonable level. Finally an implementation demonstrates that DWM4CGSP works well in CGSP and can be further extended to other similar grids

关键词： Load management grid computing Energy management Search engines Web services technology management Laboratories Monitoring Engineering management Virtual reality

Graph for science: From API based Programming to Graph Engine based Programming for HPC

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arXiv 2023年

作者： Zhang, Yu Wang, Zixiao Zhao, Jin Guo, Yuluo Yu, Hui Huang, Zhiying Shi, Xuanhua Liao, Xiaofei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Modern scientific applications predominantly run on large-scale computing platforms, necessitating collaboration between scientific domain experts and high-performance computing (HPC) experts. While domain experts are often skilled in customizing domain-specific scientific computing routines, which often involves various matrix computations, HPC experts are essential for achieving efficient execution of these computations on large-scale platforms. This process often involves utilizing complex parallel computing libraries tailored to specific matrix computation scenarios. However, the intricate programming procedure and the need for deep understanding in both application domains and HPC poses significant challenges to the widespread adoption of scientific computing. In this research, we observe that matrix computations can be transformed into equivalent graph representations, and that by utilizing graph processing engines, HPC experts can be freed from the burden of implementing efficient scientific computations. Based on this observation, we introduce a graph engine-based scientific computing (Graph for science) paradigm, which provides a unified graph programming interface, enabling domain experts to promptly implement various types of matrix computations. The proposed paradigm leverages the underlying graph processing engine to achieve efficient execution, eliminating the needs for HPC expertise in programming large-scale scientific applications. We evaluate the performance of the developed graph compute engine for three typical scientific computing routines. Our results demonstrate that the graph engine-based scientific computing paradigm achieves performance comparable to the best-performing implementations based on existing parallel computing libraries and bespoke implementations. Importantly, the paradigm greatly simplifies the development of scientific computations on large-scale platforms, reducing the programming difficulty for scientists and facilitating b

关键词： Application programming interfaces (API)

computing and Informatics: Foreword

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computing and Informatics 2008年第5期27卷 701-706页

作者： Jin, Hai You, Ilsun Guo, Minyi Lee, Deok Gyu Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Information Science Korean Bible University Korea Republic of School of Computer Science and Engineering University of Aizu Japan Information Security Technology Division Electronics and Telecommunications Research Institute Korea Republic of

HI-Net: Hyperdense inception 3D UNet for brain tumor segmentation

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arXiv 2020年

作者： Qamar, Saqib Ahmad, Parvez Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional neural networks (CNNs), mainly fully convolutional networks, have shown promising brain tumor segmentation. This paper further boosts the performance of brain tumor segmentation by proposing hyperdense inception 3D UNet (HI-Net), which captures multi-scale information by stacking factorization of 3D weighted convolutional layers in the residual inception block. We use hyper dense connections among factorized convolutional layers to extract more contexual information, with the help of features reusability. We use a dice loss function to cope with class imbalances. We validate the proposed architecture on the multi-modal brain tumor segmentation challenges (BRATS) 2020 testing dataset. Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0.79457, 0.87494, and 0.83712, respectively. © 2020, CC BY.

关键词： Convolution

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

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arXiv 2024年

作者： Luo, Zihan Song, Xiran Huang, Hong Lian, Jianxun Zhang, Chenhao Jiang, Jinqi Xie, Xing Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China The National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://***/CGCL-codes/GraphInstruct. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing Data Redundancy

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GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

International Conference on Distributed computing Systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

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VulDeePecker: A deep learning-based system for vulnerability detection

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Ou, Xinyu Jin, Hai Wang, Sujuan Deng, Zhijun Zhong, Yuyi Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Security and Computer Hebei University Shenzhen Huazhong University of Science and Technology Research Institute Department of Computer Science University of Texas at San Antonio

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products;in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with. Copyright © 2018, The Authors. All rights reserved.

关键词： computer software