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Financial resource integration algorithm of virtual enterprise in big data environment

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第4期35卷 4183-4194页

作者： Wu, Guoqiang Saghir, V Huainan Normal Univ Sch Econ & Management Huainan 232038 Peoples R China NYU Courant Inst Math Sci New York NY USA

The current resource integration algorithm lacks the consideration of users' needs, which can cause high violation of service-level agreement and poor data quality after integration. It affects the energy consumption and service quality of data center. To address this problem, a financial resource integration algorithm of virtual enterprise based on improved artificial bee colony in big data environment is proposed in this paper. The improved pagerank algorithm is used to extract the financial resource of virtual enterprise. The extracted resource is transformed. From the unified data resource centralization after transformation, service resources that satisfy users' needs and constraints are selected and combined. An improved artificial bee colony algorithm is applied to dynamically integrate service resources for different needs. Experimental results show that the proposed algorithm can effectively reduce the energy consumption of the data center, improve the data quality and user service satisfaction. The advantages and feasibility of the proposed algorithm in the integration of virtual enterprise financial resources under the big data environment are verified.

关键词： Big data environment virtual resource enterprise financial resource resource integration pagerank algorithm

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Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2021年第3期35卷 268-281页

作者： Barreda, Maria Dolz, Manuel F. Castano, M. Asuncion Univ Jaume I Castello Dept Engn & Ciencia Dels Comp Castellon De La Plana Castello Spain

Modeling the performance and energy consumption of the sparse matrix-vector product (SpMV) is essential to perform off-line analysis and, for example, choose a target computer architecture that delivers the best performance-energy consumption ratio. However, this task is especially complex given the memory-bounded nature and irregular memory accesses of theSpMV, mainly dictated by the input sparse matrix. In this paper, we propose a Machine Learning (ML)-driven approach that leverages Convolutional Neural Networks (CNNs) to provide accurate estimations of the performance and energy consumption of theSpMVkernel. The proposed CNN-based models use a blockwise approach to make the CNN architecture independent of the matrix size. These models are trained to estimate execution time as well as total, package, and DRAM energy consumption at different processor frequencies. The experimental results reveal that the overall relative error ranges between 0.5% and 14%, while at matrix level is not superior to 10%. To demonstrate the applicability and accuracy of theSpMVCNN-based models, this study is complemented with an ad-hoc time-energy model for the pagerank algorithm, a popular algorithm for web information retrieval used by search engines, which internally realizes theSpMVkernel.

关键词： Sparse matrix-vector multiplication (SpMV) performance modeling energy modeling supervised learning convolutional neural networks (CNN) pagerank algorithm

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Understanding the topological characteristics and flow complexity of urban traffic congestion

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 473卷 166-177页

作者： Wen, Tzai-Hung Chin, Wei-Chien-Benny Lai, Pei-Chun Natl Taiwan Univ Coll Sci Dept Geog 1Sec 4Roosevelt Rd Taipei 10617 Taiwan

For a growing number of developing cities, the capacities of streets cannot meet the rapidly growing demand of cars, causing traffic congestion. Understanding the spatial temporal process of traffic flow and detecting traffic congestion are important issues associated with developing sustainable urban policies to resolve congestion. Therefore, the objective of this study is to propose a flow-based ranking algorithm for investigating traffic demands in terms of the attractiveness of street segments and flow complexity of the street network based on turning probability. Our results show that, by analyzing the topological characteristics of streets and volume data for a small fraction of street segments in Taipei City, the most congested segments of the city were identified successfully. The identified congested segments are significantly close to the potential congestion zones, including the officially announced most congested streets, the segments with slow moving speeds at rush hours, and the areas near significant landmarks. The identified congested segments also captured congestion-prone areas concentrated in the business districts and industrial areas of the city. Identifying the topological characteristics and flow complexity of traffic congestion provides network topological insights for sustainable urban planning, and these characteristics can be used to further understand congestion propagation. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Traffic congestion pagerank algorithm Network topology Mobility

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A Novel Model for Predicting Essential Proteins Based on Heterogeneous Protein-Domain Network

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IEEE ACCESS 2020年 8卷 8946-8958页

作者： Chen, Zhiping Meng, Zixuan Liu, Chaoping Wang, Xiangyi Kuang, Linai Pei, Tingrui Wang, Lei Changsha Univ Coll Comp Engn & Appl Math Changsha 410022 Peoples R China Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Sec Xiangtan 411105 Peoples R China

Essential proteins play significant roles in cell survive. In current years, some Protein-Protein Interaction (PPI) data have been discovered in saccharomyces cerevisiae. Due to the high costs of biological experiments, a growing number of computational models are adopted to predict essential proteins. However, the identification accuracy of these computational models still has broad space for improvement. In this paper, a novel prediction model called NPRI is proposed to infer potential essential proteins based on the pagerank algorithm. In NPRI, a new heterogeneous Protein-Domain network will be constructed by integrating three kinds of networks such as the weighted PPI network, the Domain-Domain network and the initial Protein-Domain network first. Here, these three kinds of networks are established in accordance with gene expression data, original PPI network and known Protein-Domain network respectively. Next, based on the newly constructed heterogeneous Protein-Domain network, we will extract functional features and topological characteristics for each protein to further construct a novel distribution rate network. And then, an improved iteration method based on the pagerank algorithm will be implemented on the novel distribution rate network to infer essential proteins. Finally, in order to evaluate the performance of NPRI, we will compare NPRI with other state-of-the-art prediction models, and simulation results show that NPRI can achieve reliable identification accuracies of 90;, 84.5;and 79;in top 100, 200 and 300 predicted candidate essential proteins separately, which outperform these competitive models remarkably, and means that NPRI is a promising framework for identifying essential proteins as well.

关键词： Essential proteins protein-protein interaction network domain-domain network heterogeneous protein-domain network pagerank algorithm

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GP3D: 3D NAND Based In-Memory Graph Processing Accelerator

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2022年第2期12卷 500-507页

作者： Shim, Wonbo Yu, Shimeng Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul 01811 South Korea

In this work, we proposed GP3D: 3D NAND based processing-in-memory (PIM) accelerator for highly parallel and energy efficient graph processing. Although graph processing algorithms give informative and useful features from the real world graphs, innate randomness of the graph data pattern incurs inefficient processing in conventional computing architectures. Some previous works on nonvolatile memory (NVM) based PIM architecture have been proposed with vector-matrix multiplication (VMM) in memory array, but the limited memory capacity of the architecture causes frequent write operations on memory cells. Our GP3D architecture is based on the standard 3D NAND array structure without any structural changes which can utilize the ultra-high density of 3D NAND to store the large scale graphs in a single die. VMM operations on 3D NAND array enables efficient processing of pagerank algorithm, one of the representative algorithms of graph processing. To process the graphs with compressed format without conversion to the dense format, we exploited ternary content addressable memory (TCAM) function on 3D NAND. GP3D can eliminate the data loading burdens from storage device to memory which gives significant enhancement of the latency and energy efficiency. Evaluated by custom designed graph processing simulator, GP3D showed 30-61 times boosted speed and 18-64 times better energy efficiency over the GPU based system for various graph datasets.

关键词： Three-dimensional displays Memory management Computer architecture Arrays Virtual machine monitors Sparse matrices Computational modeling Graph processing accelerator pagerank algorithm processing-in-memory (PIM) ternary content addressable memory (TCAM) 3D NAND flash

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Critical Nodes Identification for Power Grid Based on Electrical Topology and Power Flow Distribution

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IEEE SYSTEMS JOURNAL 2023年第3期17卷 4874-4884页

作者： Fan, Bing Shu, Nan Li, Zewei Li, Fangxing North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China State Grid Hebei Elect Power Co Ltd Rongcheng Cty Power Supply Branch Rongcheng 071700 Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

The continuous development of the power industry has driven the increasing scale and complexity of power grids. There are critical nodes within these grids, the failure of which can easily lead to serious power system accidents. In order to effectively identify the critical nodes in a power grid, a critical nodes identification algorithm considering electrical topology and power flow distribution (ETPD-CNIA) is proposed. First, a node interaction model based on the line outage distribution factors is presented. Then, the model is introduced into the pagerank algorithm to calculate the node importance based on the electrical network topology. Finally, considering the node importance based on both the electrical network topology and the actual power flow distribution by an adaptive coefficient, the critical nodes are identified effectively. Simulations on the IEEE 39-bus system and the IEEE 118-bus system show that, compared with existing algorithms, the proposed ETPD-CNIA is more effective and accurate in identifying critical nodes in the power grid.

关键词： Critical nodes identification node importance pagerank algorithm power grid

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A Novel Network-Based Computational Model for Prediction of Essential Proteins

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IEEE ACCESS 2020年 8卷 138141-138148页

作者： Zhu, Xianyou Liu, Yang Pei, Tingrui Chen, Zhiping Li, Xueyong Lei, Wang Hengyang Normal Univ Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China Changsha Univ Coll Comp Engn & Appl Math Changsha 410022 Peoples R China Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Sec Xiangtan 411105 Peoples R China

Identification of essential proteins is important for understanding cell survival and development, because even if only one of these proteins is missing, organisms cannot survive or develop. Since traditional methods for identifying essential proteins based on biological experiments are costly and inefficient, more and more computational models are proposed for predicting essential proteins in recent years. In this paper, a novel computational model called BSPM is proposed, in which, an original PPI network will be built based on known protein-protein associations first, and then topology information of the original PPI network will be adopted to measure the similarities between proteins based on the SimRank algorithm. Thereafter, a weighted PPI network can be obtained based on the similarities between proteins and the original PPI network. Finally, based on the weighted PPI network, the pagerank algorithm will be used to infer potential essential proteins. Moreover, in order to evaluate the performance of BSPM, we have compared the performance of BSPM with 14 classical prediction models in the field based on two different databases, and experimental results show that BSPM can achieve prediction accuracies of 92%, 81% and 76% out of the top 100, 200 and 300 candidate proteins separately, which not only are significantly better than those 14 competitive classical prediction models, but also means that BSPM can be used as an effective model for identifying essential proteins in the future.

关键词： Proteins Predictive models Databases Computational modeling Prediction algorithms Biological system modeling Essential protein PPI SimRank algorithm pagerank algorithm

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Detecting opinion leaders in online social networks using HybridRank algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第1期35卷 513-522页

作者： Qiu Liqing Dai Jinlong Liu Haiyan Wen Yan Shandong Univ Sci & Technol Coll Comp Sci & Engn Shandong Prov Key Lab Wisdom Mine Informat Techno Qingdao Peoples R China

Opinion leaders are those users who have great influence in social networks. It is significant to detect opinion leaders for the study on social networks and other applications. According to the key idea of the pagerank algorithm, a novel algorithm called HybridRank is proposed, taking into account topic-sensitive analysis and temporal characteristics. Our two major contributions are twofold: (1) topic-sensitive analysis is conducted to obtain the clusters in social networks;(2) temporal analysis is proposed to investigate the dynamics of the user's influence over the time. We also provide impressive experimental analysis on a real dataset grabbed from Chinese Sina BBS, showing that the proposed HybridRank algorithm outperforms various related approaches.

关键词： Opinion leader pagerank algorithm topic-sensitive analysis temporal characteristics social networks

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Information credibility evaluation in online professional social network using tree augmented naive Bayes classifier

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ELECTRONIC COMMERCE RESEARCH 2021年第2期21卷 645-669页

作者： Jing, Nan Wu, Zhao Lyu, Shanshan Sugumaran, Vijayan Shanghai Univ SHU UTS SILC Business Sch Dept Informat Management Shanghai 201800 Peoples R China Oakland Univ Sch Business Adm Dept Decis & Informat Sci Rochester MI 48309 USA Oakland Univ Ctr Data Sci & Big Data Analyt Rochester MI 48309 USA

In recent years, companies depend on the Internet for posting job advertisements and attracting qualified personnel. However, with the vast number of Internet users and the tremendous amount of information on the Internet, it is difficult to accurately evaluate the credibility of the information that candidates provide on the Internet. Therefore, we propose an approach to assess information credibility in terms of trustworthiness and authority to identify unreliable user profiles in online professional social networks. Our approach calculates the trustworthiness probabilities of user profile information using the Tree Augmented Naive Bayes (TAN) classifier. It also measures the authority of individual users by applying the pagerank algorithm for analyzing the user interactions in the professional social networks. Finally, a group of LinkedIn users' profiles is selected for conducting experiments to validate the proposed approach. Experiments based on a real-world scenario show that our approach integrating the TAN Bayes and pagerank algorithm outperforms other existing approaches in classification accuracy. In addition, the approach has been applied to another social network, namely, Maimai in China to further demonstrate its usefulness.

关键词： Professional social networks Information credibility Trustworthiness probability Tree augmented naive Bayes classifier pagerank algorithm

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Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation

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BMC MEDICAL GENOMICS 2014年第2-Sup期7卷 S4-S4页

作者： Li, Min Zhang, Jiayi Liu, Qing Wang, Jianxin Wu, Fang-Xiang Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Univ Saskatchewan Engn Coll Saskatoon SK Canada

Background: Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN). Results: Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A pagerank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the pagerank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia. Conclusions: Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The pagerank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.

关键词： Protein Interaction Network pagerank algorithm Human Protein Interaction Network Weighted Degree Centrality pagerank Method

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