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arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Erratum: First Fermi-LAT Solar Flare Catalog (2021, ApJS, 252, 13)

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The Astrophysical Journal Supplement Series 2021年第1期256卷

作者： M. Ajello L. Baldini D. Bastieri R. Bellazzini A. Berretta E. Bissaldi R. D. Blandford R. Bonino P. Bruel S. Buson R. A. Cameron R. Caputo E. Cavazzuti C. C. Cheung G. Chiaro D. Costantin S. Cutini F. D'Ammando F. de Palma R. Desiante N. Di Lalla L. Di Venere F. Fana Dirirsa S. J. Fegan Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini F. Giordano M. Giroletti D. Green S. Guiriec E. Hays J. W. Hewitt D. Horan G. Jóhannesson M. Kovac'evic' M. Kuss S. Larsson L. Latronico J. Li F. Longo M. N. Lovellette P. Lubrano S. Maldera A. Manfreda G. Martí-Devesa M. N. Mazziotta I. Mereu P. F. Michelson T. Mizuno M. E. Monzani A. Morselli I. V. Moskalenko M. Negro N. Omodei M. Orienti E. Orlando D. Paneque Z. Pei M. Persic M. Pesce-Rollins V. Petrosian F. Piron T. A. Porter G. Principe J. L. Racusin S. Rainò R. Rando B. Rani M. Razzano S. Razzaque A. Reimer O. Reimer D. Serini C. Sgrò E. J. Siskind G. Spandre P. Spinelli D. Tak E. Troja J. Valverde K. Wood G. Zaharijas Department of Physics and Astronomy Clemson University Kinard Lab of Physics Clemson SC 29634-0978 USA Università di Pisa and Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica e Astronomia 'G. Galilei ' Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy melissa.pesce.rollins@pi.infn.it Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Dipartimento di Fisica 'M. Merlin' dell'Università e del Politecnico di Bari via Amendola 173 I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari I-70126 Bari Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford CA 94305 USA nicola.omodei@stanford.edu vahep@stanford.edu Istituto Nazionale di Fisica Nucleare Sezione di Torino I-10125 Torino Italy Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 F-91128 Palaiseau France Institut für Theoretische Physik und Astrophysik Universität Würzburg Campus Hubland Nord Emil-Fischer-Str. 31 D-97074 Würzburg Germany NASA Goddard Space Flight Center Greenbelt MD 20771 USA Italian Space Agency Via del Politecnico snc I-00133 Roma Italy Space Science Division Naval Research Laboratory Washington DC 20375-5352 USA INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 I-20133 Milano Italy University of Padua Department of Statistical Science Via 8 Febbraio 2 35122 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy INAF Istituto di Radioastronomia I-40129 Bologna Italy Department of Physics University of Johannesburg P.O. Box 524 Auckland Park 2006 So

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A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use

A Prediction Modelling and Pattern Detection Approach for th...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Wajdi Alghamdi Daniel Stamate Katherine Vang Daniel Stahl Marco Colizzi Giada Tripoli Diego Quattrone Olesya Ajnakina Robin M. Murray Marta Di Forti Data Science & Soft Computing Lab. and Department of Computing Goldsmiths. University of London Department of Rinstatistics Institute of Psychiatry Psychology and Neuroscience London MRC Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience London Department of Psychosis Studies Institute of Psychology and Neuroscience London

ISBN: (纸本)9781509061686

Over the last two decades, a significant body of research has established a link between cannabis use and psychotic outcomes. In this study, we aim to propose a novel symbiotic machine learning and statistical approach to pattern detection and to developing predictive models for the onset of first-episode psychosis. The data used has been gathered from real cases in cooperation with a medical research institution, and comprises a wide set of variables including demographic, drug-related, as well as several variables specifically related to the cannabis use. Our approach is built upon several machine learning techniques whose predictive models have been optimised in a computationally intensive framework. The ability of these models to predict first-episode psychosis has been extensively tested through large scale Monte Carlo simulations. Our results show that Boosted Classification Trees outperform other models in this context, and have significant predictive ability despite a large number of missing values in the data. Furthermore, we extended our approach by further investigating how different patterns of cannabis use relate to new cases of psychosis, via association analysis and Bayesian techniques.

关键词： Predictive models Computational modeling data models Psychiatry Monte Carlo methods Bayes methods Machine learning algorithms

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Erratum to: Improving official statistics in emerging markets using machine learning and mobile phone data

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EPJ data science 2017年第1期6卷 1-1页

作者： Jahani, Eaman Sundsøy, Pål Bjelland, Johannes Bengtsson, Linus Pentland, Alex ‘Sandy’ de Montjoye, Yves-Alexandre Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge USA Telenor Research Oslo Norway Flowminder Stockholm Sweden Department of Public Health Sciences Karolinska Institute Stockholm Sweden Media Lab Massachusetts Institute of Technology Cambridge USA Department of Computing Imperial College London London UK Data Science Institute William Penney Laboratory Imperial College London London UK

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Image segmentation using membrane computing: A literature survey 11th

Image segmentation using membrane computing: A literature su...

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11th International Conference on Bio-inspired computing – Theories and Applications, BIC-TA 2016

作者： Yahya, Rafaa I. Shamsuddin, Siti Mariyam Yahya, Salah I. Hasan, Shafatnnur Al-Salibi, Bisan Al-Khafaji, Ghada Faculty of Computing Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Computer College of Science University of Al-Mustansiriyah Baghdad Iraq UTM Big Data Center Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Software Engineering Koya University Iraq School of Computer Sciences Universiti Sains Malaysia USM GelugorPenang11800 Malaysia Department of Computer Science College of Science Baghdad University Baghdad Iraq

ISBN: (纸本)9789811036101

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Image segmentation

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Bright γ-ray flares observed in GRB131108A

arXiv

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arXiv 2019年

作者： Ajello, M. Arimoto, M. Asano, K. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Chiaro, G. Ciprini, S. Costantin, D. Cutini, S. D'Ammando, F. de la Torre Luque, P. de Palma, F. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Franckowiak, A. Fukazawa, Y. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Grondin, M.-H. Guiriec, S. Hays, E. Horan, D. Jóhannesson, G. Kocevski, D. Kovac'evic, M. Kuss, M. Larsson, S. Latronico, L. Li, J. Liodakis, I. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. McEnery, J.E. Mereu, I. Michelson, P.F. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Ohno, M. Omodei, N. Orienti, M. Orlando, E. Palatiello, M. Paliya, V.S. Paneque, D. Pei, Z. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Reimer, A. Reimer, O. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tajima, H. Takagi, K. Tak, D. Torres, D.F. Valverde, J. Wood, K. Yamazaki, R. Yassine, M. Zhu, S. Lucas Uhm, Z. Zhang, Bing Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Ishikawa Kanazawa920-1192 Japan Institute for Cosmic-Ray Research University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Department of Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 Italy Dipartimento di Fisica "M. Merlin" dell'Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany Insti

GRB 131108A is a bright long Gamma-Ray Burst (GRB) detected by the Large Area Telescope and the Gamma-ray Burst Monitor on board the Fermi Gamma-ray Space Telescope. Dedicated temporal and spectral analyses reveal three γ-ray flares dominating above 100 MeV, which are not directly related to the prompt emission in the GBM band (10 keV-10 MeV). The high-energy light curve of GRB 131108A (100 MeV - 10 GeV) shows an unusual evolution: a steep decay, followed by three flares with an underlying emission, and then a long-lasting decay phase. The detailed analysis of the γ-ray flares finds that the three flares are 6-20 times brighter than the underlying emission and are similar to each other. The fluence of each flare, (1.6 ∼ 2.0) × 10−6 erg cm−2, is comparable to that of emission during the steep decay phase, 1.7 × 10−6 erg cm−2. The total fluence from three γ-ray flares is 5.3 × 10−6 erg cm−2. The three γ-ray flares show properties similar to the usual X-ray flares that are sharp flux increases, occurring in ∼ 50% of afterglows, in some cases well after the prompt emission. Also, the temporal and spectral indices during the early steep decay phase and the decaying phase of each flare show the consistency with a relation of the curvature effect (Equation presented), which is the first observational evidence of the high-latitude emission in the GeV energy band. Copyright © 2019, The Authors. All rights reserved.

关键词： Gamma rays

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Unsupervised classification of data streams based on typicality and eccentricity data analytics

Unsupervised classification of data streams based on typical...

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2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

作者： Costa, Bruno Sielly Jales Bezerra, Clauber Gomes Guedes, Luiz Affonso Angelov, Plamen Parvanov Federal Institute of Rio Grande do Norte - IFRN Campus Natal - Zona Norte Natal Brazil Federal Institute of Rio Grande do Norte - IFRN Campus EaD Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Brazil Data Science Group School of Computing and Communications Lancaster University LancasterLA14WA United Kingdom Department of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781509006250

In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns "on-The-fly". It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtime constraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handle concept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from the data. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilot plant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to different problems and with much higher dimensional inputs. The results obtained from the real data are very significant. © 2016 IEEE.

关键词： Classification (of information)

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Ebolavirus database: Gene and protein information resource for ebolaviruses

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Advances in Bioinformatics 2016年第1期2016卷 1673284页

作者： Swetha, Rayapadi G. Ramaiah, Sudha Anbarasu, Anand Sekar, Kanagaraj Medical and Biological Computing Laboratory School of Biosciences and Technology VIT University Vellore 632 014 India Laboratory for Structural Biology and Bio-Computing Department of Computational and Data Sciences Indian Institute of Science Bangalore 560 012 India

Ebola Virus Disease (EVD) is a life-threatening haemorrhagic fever in humans. Even though there are many reports on EVD, the protein precursor functions and virulent factors of ebolaviruses remain poorly understood. Comparative analyses of Ebolavirus genomes will help in the identification of these important features. This prompted us to develop the Ebolavirus database (EDB) and we have provided links to various tools that will aid researchers to locate important regions in both the genomes and proteomes of Ebolavirus. The genomic analyses of ebolaviruses will provide important clues for locating the essential and core functional genes. The aim of EDB is to act as an integrated resource for ebolaviruses and we strongly believe that the database will be a useful tool for clinicians, microbiologists, health care workers, and bioscience researchers. © 2016 Rayapadi G. Swetha et al.

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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ACID encountering the CAP theorem: Two bank case studies 12

ACID encountering the CAP theorem: Two bank case studies

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12th Web Information System and Application Conference, WISA 2015

作者： Kong, Chao Gao, Ming Qian, Weining Zhou, Minqi Gong, Xueqing Zhang, Rong Zhou, Aoying Institute for Data Science and Engineering and Shanghai Key Lab for Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9781467393720

In the era of big data, we may adopt the distributed architecture for a transaction processing system due to some reasons, including distributed branches, heavy demand and operational expenditure, etc. In terms of the CAP Theorem, a transaction processing system associated with ACID properties is infeasible to work well in the distributed architecture. It is indispensable to address how to make a trade-off between availability and partition tolerance for a bank as it favors the consistency in the distributed system. In this research, we conduct two case studies to address the question using two transaction logs collected from a bank in China. We mainly analyze the table dependency and the table concurrency, and find that (1) it is arduous to partition the data in the database system associated with ACID properties, (2) in-memory architecture for updating transactions may be an alternative for building a transaction processing system. © 2015 IEEE.

关键词： Memory architecture

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