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International Conference on systems and Informatics (ICSAI)

作者： Yukun Wang Kai Lei Jincheng Wu Xiaohui Huang Yuming Lu Institute of Big Data Technologies Peking University Shenzhen P.R. China Shenzhen Cloud Computing Center National Supercomputing Center in Shenzhen Shenzhen P.R. China Shenzhen Key Lab for Cognitive Services Engineering Shenzhen Institute of Information Technology Shenzhen P.R. China

The e-government system plays a prominent part in government's office work, however, in the development process, there exist some problems currently, such as repeated development of common functions but hard to satisfy various personalized needs, independence of different systems which makes information difficult to share. These problems have seriously hindered development progress, increased development cost, and may result in information isolated islands. To solve these problems, this paper proposes an e-government oriented BPM OA platform, and introduces its design and implementation process. To achieve rapid development, by analysis and abstraction, we extract main features of common e-government systems and make them into configurable functions. The powerful form builder, the explicit authority management method and the BPMN 2.0 based workflow engine can significantly simplify the development and deployment process, promote the inter-system information exchange, and improve development efficiency.

关键词： Engines Electronic government Process control Tools Monitoring

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Communication Cost Efficient Virtualized Network Function Placement for big data Processing

Communication Cost Efficient Virtualized Network Function Pl...

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IEEE Conference on Computer Communications Workshops

作者： Lin Gu Sheng Tao Deze Zeng Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan Hubei China

ISBN: (纸本)9781467399562

The data explosion in the emerging big data era imposes a big burden on the network infrastructure. This vision has urged the evolution of computer networks. By softwarizing traditional dedicated hardware based functions to virtualized network function (VNF) that can run on standard commodity servers, network function virtualization (NFV) technology promises increased networking efficiency, flexibility and scalability. From the perspective of network service providers, with the consideration of big data traffic volume, one primary concern is on the communication cost, which is highly influenced by the VNF placement strategy. In this paper, we are motivated to investigate the issue on communication cost efficient VNF placement problem for big data processing, with joint consideration of network flow balancing and the predetermined network service semantics. We formulate this problem into a mixed-integer linear programming (MILP) form and then propose a low-complexity relaxation-based heuristic algorithm accordingly. The high efficiency of our proposal is validated by extensive simulation studies.

关键词： Noise measurement big data Distributed databases Algorithm design and analysis Servers Heuristic algorithms Semantics

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LS-AMS: An Adaptive Indexing Structure for Realtime Search on Microblogs

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IEEE Transactions on big data 2015年第4期1卷 125-137页

作者： Zhao, Feng Liu, Jun Zhou, Jingyu Jin, Hai Yang, Laurence T. Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technolgoy Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science St. Francis Xavier University Canada

Indexing microblogs for realtime search is challenging, because new microblogs are created at tremendous speed, and user query requests keep constantly changing. To guarantee user obtain complete query results, micro-blogging site maintains huge indices which leads to index fragmentation or extra merging overhead during realtime search. This paper proposes an efficient Log-Structured index structure with Adaptive Merging Strategy (LS-AMS) for realtime search on microblogs. LS-AMS structure consists of an inverted index buffer and a sequence of dynamically adjustable index packages with exponentially increasing sizes. These index packages manage their inverted indices using adaptive merging strategy, which can reduce the merging overhead to improve query performance and can adjust the index structure based on environmental factors, such as the arrival rate of query requests and new microblogs. Experimental results show that LS-AMS can greatly improve query performance without increasing the update cost and improve the self-adaptability in dynamic environment. © 2015 IEEE.

关键词： Merging

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Scheme for Ensuring data Security on Cloud data Storage in a Semi-trusted Third Party Auditor

Scheme for Ensuring Data Security on Cloud Data Storage in a...

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International Conference on Computer Science and Network technology

作者： Zaid Alaa Hussien Hai Jin Zaid Ameen Abduljabbar Mohammed Abdulridha Hussain Salah H. Abbdal Deqing Zou Cluster and Grid Computing Lab Big Data Technology and System Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781467381741

Cloud computing is a promising information technology service that allocates and reallocates resources when a client requires virtual data storage and network facility at any time and place. Cloud computing provides large data storage and management services at the lowest cost. Thus, it is crucial for many organizations and clients who seek such services. data security and integrity are necessary and play important roles. However, these two issues are facing significant challenges. Therefore, a robust scheme is required to ensure security and privacy while transmitting or storing data in the cloud storage environment. We propose an efficient and robust scheme to ensure data security in a semi-trusted third party auditor. Our scheme adopts an advanced encryption standard to support data owner privacy, a cryptography hash function to maintain data owner integrity, and elliptic curve cryptography to ensure data confidentiality, correctness, and security when transmitting data over unsecure channels. A security analysis confirms that our scheme can withstand man-in-the-middle attack and provides data correctness.

关键词： Cloud storage data security elliptic curve cryptography hash function integrity semi-trusted third party auditor data Security Elliptic curve cryptography cryptography Cloud computing computer information security data Storage Scheme auditor Third party customers Management services Transmitting data

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Self-Adaptive Extracting Academic Entities from World Wide Web

Self-Adaptive Extracting Academic Entities from World Wide W...

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International Conference on Collaborative computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Pingpeng Yuan Yi Li Hai Jin Ling Liu Services Computing Technology and System Lab. Huazhong University of Science and Technology Wuhan China Distributed Data Intensive Systems Lab. Georgia Institute of Technology Atlanta Georgia

Huge amount of entities and theirs relationships are posted on the Web. Those entities and theirs relationship networks help many activities. In this paper, we focus on the task of extracting academic entity network from homepages. Homepages usually contain many entities, such as persons, conference/journal and organization and theirs relationship. However, homepages don't follow a unified layout format and often contains similar information, but differs greatly in layouts and styles, which makes it impossible to use a unified set of rules to handle them all. Thus we propose an integrated approach to automatically extract data from unstructured texts. The main idea of the approach is to adopt the most suitable approach to extract entities. Thus, the approach is self-adaptive. Firstly, the approach decomposes web pages into text units and then classifier is used to determine units' type. Once the units' types are known, the different technologies are chosen to deal with them. For example, edit distance and inverted index are used to identify names etc. And Conditional Random Field technology is considered the best solution to extract publication entries. The result shows that LineX has achieved high performance on extracting entities from web pages in academic community.

关键词： Web pages data mining Knowledge based systems Electronic mail HTML Internet Organizations

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Sparse Online Relative Similarity Learning

Sparse Online Relative Similarity Learning

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IEEE International Conference on data Mining (ICDM)

作者： Dezhong Yao Peilin Zhao Chen Yu Hai Jin Bin Li Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China Data Analytics Department Institute for Infocomm Research Singapore Economics and Management School Wuhan University Wuhan China

ISBN: (纸本)9781467395052

For many data mining and machine learning tasks, the quality of a similarity measure is the key for their performance. To automatically find a good similarity measure from datasets, metric learning and similarity learning are proposed and studied extensively. Metric learning will learn a Mahalanobis distance based on positive semi-definite (PSD) matrix, to measure the distances between objectives, while similarity learning aims to directly learn a similarity function without PSD constraint so that it is more attractive. Most of the existing similarity learning algorithms are online similarity learning method, since online learning is more scalable than offline learning. However, most existing online similarity learning algorithms learn a full matrix with d 2 parameters, where d is the dimension of the instances. This is clearly inefficient for high dimensional tasks due to its high memory and computational complexity. To solve this issue, we introduce several Sparse Online Relative Similarity (SORS) learning algorithms, which learn a sparse model during the learning process, so that the memory and computational cost can be significantly reduced. We theoretically analyze the proposed algorithms, and evaluate them on some real-world high dimensional datasets. Encouraging empirical results demonstrate the advantages of our approach in terms of efficiency and efficacy.

关键词： Measurement Sparse matrices Image retrieval Machine learning algorithms Algorithm design and analysis data mining Correlation

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Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

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Molecular psychiatry 2024年第4期29卷 1216页

作者： Bo-Gyeom Kim Gakyung Kim Yoshinari Abe Pino Alonso Stephanie Ameis Alan Anticevic Paul D Arnold Srinivas Balachander Nerisa Banaj Nuria Bargalló Marcelo C Batistuzzo Francesco Benedetti Sara Bertolín Jan Carl Beucke Irene Bollettini Silvia Brem Brian P Brennan Jan K Buitelaar Rosa Calvo Miguel Castelo-Branco Yuqi Cheng Ritu Bhusal Chhatkuli Valentina Ciullo Ana Coelho Beatriz Couto Sara Dallaspezia Benjamin A Ely Sónia Ferreira Martine Fontaine Jean-Paul Fouche Rachael Grazioplene Patricia Gruner Kristen Hagen Bjarne Hansen Gregory L Hanna Yoshiyuki Hirano Marcelo Q Höxter Morgan Hough Hao Hu Chaim Huyser Toshikazu Ikuta Neda Jahanshad Anthony James Fern Jaspers-Fayer Selina Kasprzak Norbert Kathmann Christian Kaufmann Minah Kim Kathrin Koch Gerd Kvale Jun Soo Kwon Luisa Lazaro Junhee Lee Christine Lochner Jin Lu Daniela Rodriguez Manrique Ignacio Martínez-Zalacaín Yoshitada Masuda Koji Matsumoto Maria Paula Maziero Jose M Menchón Luciano Minuzzi Pedro Silva Moreira Pedro Morgado Janardhanan C Narayanaswamy Jin Narumoto Ana E Ortiz Junko Ota Jose C Pariente Chris Perriello Maria Picó-Pérez Christopher Pittenger Sara Poletti Eva Real Y C Janardhan Reddy Daan van Rooij Yuki Sakai João Ricardo Sato Cinto Segalas Roseli G Shavitt Zonglin Shen Eiji Shimizu Venkataram Shivakumar Noam Soreni Carles Soriano-Mas Nuno Sousa Mafalda Machado Sousa Gianfranco Spalletta Emily R Stern S Evelyn Stewart Philip R Szeszko Rajat Thomas Sophia I Thomopoulos Daniela Vecchio Ganesan Venkatasubramanian Chris Vriend Susanne Walitza Zhen Wang Anri Watanabe Lidewij Wolters Jian Xu Kei Yamada Je-Yeon Yun Mojtaba Zarei Qing Zhao Xi Zhu Paul M Thompson Willem B Bruin Guido A van Wingen Federica Piras Fabrizio Piras Dan J Stein Odile A van den Heuvel Helen Blair Simpson Rachel Marsh Jiook Cha Department of Psychology College of Social Sciences Seoul National University Seoul Republic of Korea. Department of Brain and Cognitive Sciences College of Natural Sciences Seoul National University Seoul Republic of Korea. Graduate School of Medical Science Kyoto Prefectural University of Medicine Department of Psychiatry Kyoto City Japan. Bellvitge Biomedical Research Insitute-IDIBELL Bellvitge University Hospital Barcelona Spain. CIBER of Mental Health (CIBERSAM) Carlos III Health Institute Madrid Spain. Department of Clinical Sciences University of Barcelona Barcelona Spain. The Margaret and Wallace McCain Centre for Child Youth & Family Mental Health and Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health Toronto ON Canada. Department of Psychiatry University of Toronto Toronto ON Canada. Program in Neurosciences and Mental Health The Hospital for Sick Children Toronto ON Canada. Department of Psychiatry Yale University School of Medicine New Haven CT 06510 USA. The Mathison Centre for Mental Health Research & Education Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary AB Canada. Departments of Psychiatry and Medical Genetics Cumming School of Medicine University of Calgary Calgary AB Canada. OCD clinic Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore India. Laboratory of Neuropsychiatry Department of Clinical and Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy. Center of Image Diagnostic Hospital Clínic de Barcelona Barcelona Spain. Magnetic Resonance Image Core Facility Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain. Departamento e Instituto de Psiquiatria do Hospital das Clinicas IPQ HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. Department of Methods and Techniques in Psychology Pontifical Catholic University São Paulo SP

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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AMSPM: Adaptive Model Selection and Partition Mechanism for Edge Intelligence-driven 5G Smart City with Dynamic computing Resources

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ACM Transactions on Sensor Networks 1000年

作者： Xin Niu Xuejiao Cao Chen Yu Hai Jin 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

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the sustainability of intelligent services in smart city, we propose the Adaptive Model Selection and Partition Mechanism (AMSPM) in 5G smart city where EI provides services, which mainly consists of Adaptive Model Selection (AMS) and Adaptive Model Partition (AMP). In AMSPM, the model selection and partition of deep neural network (DNN) are formulated as an optimization problem. Firstly, we propose a recursive-based algorithm named AMS based on the computing resources of edge devices to derive an appropriate DNN model that satisfies the latency demand of intelligent services. Then, we adaptively partition the selected DNN model according to the computing resources of edge devices. The experimental results demonstrate that, when compared with state-of-the-art model selection and partition mechanisms, AMSPM not only reduces latency but also enhances computing resource utilization.

关键词： Edge intelligence changing available computing resources adaptive model selection and partition smart city sustainable intelligent services

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Cheetah: Accelerating Dynamic Graph Mining with Grouping Updates

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ACM Transactions on Architecture and Code Optimization 1000年

作者： Yi Zhang Xiaomeng Yi Yu Huang Jingrui Yuan Chuangyi Gui Dan Chen Long Zheng Jianhui Yue Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Software Engineering Huazhong University of Science and Technology Wuhan China Eastern Institute of Technology Ningbo China National University of Singapore Singapore Singapore Michigan Technological University Houghton United States School of Computer Science and Engieering UNSW Sydney Kensington Australia

Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.

关键词： Graph mining system dynamic graph incremental computing

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