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Journal of Information Hiding and Multimedia Signal Processing 2017年第3期8卷 640-648页

作者： Jiang, Hua Zou, Fumin Lin, Rongde Li, Lingxiang Key Lab of Granular Computing Minnan Normal University ZhangzhouFujian China Beidou Navigation and Smart Traffic Innovation Center of Fujian Province FuzhouFujian China School of Mathematical Science Huaqiao university QuanzhouFujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications FuzhouFujian China School of Electronics and Information Engineering Hunan University of Science and Engineering YongzhouHunan China

Automata theory is an important branch of theoretical computer science. ω-automaton is an important part of automata theory. The judgment standard for the equivalence of two automata is the equivalence of their acceptance language. Acceptance conditions for automata are expressed with state set. This paper tries to use path to express acceptance conditions for ω-automaton that is convenient for judging the automatons equivalence property and transforming between different types of automata. With the help of Path ω-automaton, Muller automaton is transformed into Büchi automaton. Compared with the existing algorithm, the state scale of Büchi automaton has been reduced and the problem of state space explosion has been relieved. The research findings in this paper contribute to enrich the existing ω-automaton theory and provide a new idea for ω-automaton theory and application research. © 2017.

关键词： Automata theory

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Benchmarking the availability and fault tolerance of Cassandra

Benchmarking the availability and fault tolerance of Cassand...

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7th International Workshop on big data Benchmarking, WBDB 2015

作者： Rosselli, Marten Niemann, Raik Ivanov, Todor Tolle, Karsten Zicari, Roberto V. Frankfurt Big Data Lab Goethe University Frankfurt am Main Frankfurt Germany Accenture Germany Frankfurt Germany Institute of Information Systems University of Applied Science Hof Hof Germany

ISBN: (纸本)9783319497471

To be able to handle big data workloads, modern NoSQL database management systems like Cassandra are designed to scale well over multiple machines. However, with each additional machine in a cluster, the likelihood for hardware failure increases. In order to still achieve high availability and fault tolerance, the data needs to be replicated within the cluster. Predictable and stable response times are required by many applications even in the case of a node failure. While Cassandra guarantees high availability, the influence of a node failure on the system performance is still unclear. In this paper, we therefore focus on the availability and fault tolerance of Cassandra. We analyze the impact of a node outage within a Cassandra cluster on the throughput and latency for different workloads. Our results show that Cassandra is well suited to achieve high availability while preserving table response times in case of a node failure. Especially for read intensive applications that require high availability, Cassadra is a good choice. © Springer International Publishing AG 2016.

关键词： Fault tolerance

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Application of Graph Theory to Evaluate Chemical Reactions in Cells

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Journal of Physics: Conference Series 2019年第1期1391卷

作者： Sachiyo Aburatani Yuichi Kokabu Ryota Teshima Teppei Ogawa Michihiro Araki Tomokazu Shiarai Computational Bio Big Data Open Innovation Lab. (CBBD-OIL) National Institute of Advanced Industrial Science and Technology (AIST) AIST Tokyo Waterfront Main Bldg. 2-3-26 Aomi Koto-ku Tokyo 135-0064 Japan Bioscience Department Mitsui Knowledge Industry Co. Ltd. Atago Green Hills MORI Tower 2-5-1 Atago Minato-ku Tokyo 105-6215 Japan Graduate School of Medicine Kyoto University 54 Kawahara-cho Shogoin Sakyo-ku Kyoto 606-8507 Japan Center for Sustainable Resource Science RIKEN 1-7-22 Suehiro-cho Tsurumi-ku Yokohama Kanagawa 230-0045 Japan

Chemical reactions occur in cells for survival and adaptation to various conditions. After these chemical reactions, the reactants and products are often sequentially modified through metabolic pathways. In this study, we defined new features to evaluate the possibility of such inferred metabolic pathways. We focused on the main chain structure of a compound as a non-directional graph, and developed a method to define the similarity between these main chain structure graphs. In this study, we defined four features: 1) the number of main chain graph nodes, 2) the graphical density of the main chain graph, 3) the chemical density of the main chain, and 4) the graph centrality of the reaction group in the main chain graph. We defined the main chain structures of about 16,000 chemical compounds, and calculated the values of the four features by the defined equations for each compound. Finally, we calculated the correlation coefficients between all chemical compound pairs from the four defined features. A comparison of the similarities of the main chain graphs between known chemical reactions revealed that our defined features are suitable for detecting the possible reactions.

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Semantic mapping: Towards contextual and trend analysis of behaviours and practices

Semantic mapping: Towards contextual and trend analysis of b...

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2016 Working Notes of Conference and labs of the Evaluation Forum, CLEF 2016

作者： Murtagh, Fionn Big Data Lab Department of Computer Science and Mathematics University of Derby DerbyDE22 1GB United Kingdom Department of Computing Goldsmiths University of London LondonSW14 6NW United Kingdom

As a platform for unsupervised data mining and pattern recognition, we use Correspondence Analysis on Twitter content from May to December 2015. The following data characteristics are well addressed: exponentially distributed data properties, and major imbalance between categories. Contextualization is supported. To both focus on informative resolution scale in one's data, and to handle large data sets, the granularity of point clouds offers benefits.

关键词： data mining

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Overview of the CL-SciSumm 2016 Shared Task

Overview of the CL-SciSumm 2016 Shared Task

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Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries, BIRNDL 2016

作者： Jaidka, Kokil Chandrasekaran, Muthu Kumar Rustagi, Sajal Kan, Min-Yen Big Data Experience Lab Adobe Research India India School of Computing National University of Singapore Singapore Singapore Dept. of Computer Science and Engineering Indian Institute of Technology Roorkee India Interactive and Digital Media Institute National University of Singapore Singapore Singapore

The CL-SciSumm 2016 Shared Task is the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. The task built off of the experience and training data set created in its namesake pilot task, which was conducted in 2014 by the same organizing committee. The track included three tasks involving: (1A) identifying relationships between citing documents and the referred document, (1B) classifying the discourse facets, and (2) generating the abstractive summary. The dataset comprised 30 annotated sets of citing and reference papers from the open access research papers in the CL domain. This overview paper describes the participation and the official results of the second CL-SciSumm Shared Task, organized as a part of the Joint Workshop onBibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016), held in New Jersey,USA in June, 2016. The annotated dataset used for this shared task and the scripts used for evaluation can be accessed and used by the community at: https://***/WING-NUS/scisumm-corpus.

关键词： Digital libraries

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Piggyback game: Efficient event stream dissemination in Online Social Network systems

Piggyback game: Efficient event stream dissemination in Onli...

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International Conference on Network Protocols

作者： Fan Zhang Hanhua Chen Hai Jin Services Computing Technology and System Lab Big Data Technology and System Lab and Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509032822

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffics due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server messages for event stream dissemination. Different sub-graph structures are exploited for achieving the approximated optimal assignment. However, such schemes incur high costs of computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the high complex graph computation problem into individuals' rational strategy selection of each node. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existing of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimal. We conduct comprehensive experiments to evaluate the performance of this design using large-scale real-world traces from popular OSN systems. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.

关键词： Games Schedules Heuristic algorithms Algorithm design and analysis Nash equilibrium Social network services Servers

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Software Defined Networking Based On-Demand Routing Protocol in Vehicle Ad Hoc Networks

Software Defined Networking Based On-Demand Routing Protocol...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Baihong Dong Weigang Wu Zhiwei Yang Junjie Li School of Data and Computer Science Sun Yat-Sen University Guangdong Key Lab. of Big Data Analysis and Processing Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou

ISBN: (纸本)9781509056972

This paper comes up with a SDN based On-Demand Routing Protocol, SVAO, which separates data forwarding layer and network control layer, as in SDN, to enhance the data transmission efficiency within VANETs. The Roadside Service Unit plays the role of Local Controller and is in charge of selecting vehicles to forward packet within a road segment. All the vehicles state in the road. Correspondingly, a two-level design is used. Global Level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded. And the Local Level is in charge of selecting forwarding vehicles in each road segment determined by the global level. We compare SVAO with popular ad-hoc network routing protocols, including OLSR, DSR, DSDV, and DB via simulations. We consider the impact of vehicle density, speed on data transmission rate and average packet delay. The simulation results show that in case of large network scales or high vehicle speed, SVAO performs better than the others.

关键词： Routing protocols Roads Routing Ad hoc networks Topology Vehicle dynamics

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Robust frequent directions with application in online learning

arXiv

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arXiv 2017年

作者： Luo, Luo Chen, Cheng Zhang, Zhihua Li, Wu-Jun Zhang, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University 5 Yiheyuan Road Beijing100871 China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University 163 Xianlin Avenue Nanjing210023 China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Heuristic methods

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Analyzing information sharing strategies of users in online social networks

Analyzing information sharing strategies of users in online ...

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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

作者： Nguyen, Dong-Anh Tan, Shulong Ramanathan, Ram Yan, Xifeng Department of Computer Science University of California Santa BarbaraCA93106 United States Baidu Big Data Lab. 1195 Bordeaux Drive SunnyvaleCA94089 United States Network Research Department Raytheon BBN Technologies 10 Moulton Street CambridgeMA02138 United States

ISBN: (纸本)9781509028467

User information sharing is an important behavior in online social networks. Understanding such behavior could help in various applications such as user modeling, information cascade analysis, viral marketing, etc. In this paper, we aim to understand the strategies users employ to make retweet decision. We are interested in investigating whether these strategies in online social network contain significant information about users and can be used to further characterize users. We propose a flexible model that captures a number of behavior signals affecting user's retweet decision. Our empirical results show that the inferred strategies can help increase the performance of retweet prediction. © 2016 IEEE.

关键词： Social networking (online)

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