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نتیجه جستجو - مقاله انگیسی رایگان داده های بزرگ 2017

تعداد مقالات یافته شده: 134
ردیف عنوان نوع
1 Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases
استفاده از داده های بزرگ برای کشف تشخیص و درمان برای دستگاه گوارش و کبد-2017
Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.
Keywords: Big Data | Translational Bioinformatics | Drug Repurposing | Precision Medicine.
مقاله انگلیسی
2 Utilizing Cloud Computing to address big geospatial data challenges
استفاده از محاسبات ابری برای پرداختن به چالش های داده های جغرافیایی بزرگ-2017
Big Data has emerged with new opportunities for research, development, innovation and business. It is charac- terized by the so-called four Vs: volume, velocity, veracity and variety and may bring significant value through the processing of Big Data. The transformation of Big Datas 4 Vs into the 5th (value) is a grand challenge for pro- cessing capacity. Cloud Computing has emerged as a new paradigm to provide computing as a utility service for addressing different processing needs with a) on demand services, b) pooled resources, c) elasticity, d) broad band access and e) measured services. The utility of delivering computing capability fosters a potential solution for the transformation of Big Datas 4 Vs into the 5th (value). This paper investigates how Cloud Computing can be utilized to address Big Data challenges to enable such transformation. We introduce and review four geospatial scientific examples, including climate studies, geospatial knowledge mining, land cover simulation, and dust storm modelling. The method is presented in a tabular framework as a guidance to leverage Cloud Computing for Big Data solutions. It is demostrated throught the four examples that the framework method supports the life cycle of Big Data processing, including management, access, mining analytics, simulation and forecasting. This tabular framework can also be referred as a guidance to develop potential solutions for other big geospatial data challenges and initiatives, such as smart cities.© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).
Keywords: Big Data | Cloud Computing | Spatiotemporal data | Geospatial science | Smart cities
مقاله انگلیسی
3 ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data
ViSiBiD: مدل های یادگیری برای کشف زودرس و پیش بینی زمان واقعی از حوادث بالینی شدید با استفاده از علائم حیاتی به عنوان داده های بزرگ-2017
The advance in wearable and wireless sensors technology have made it possible to monitor multiple vital signs (e.g. heart rate, blood pressure) of a patient anytime, anywhere. Vital signs are an essential part of daily monitoring and disease prevention. When multiple vital sign data from many patients are accumulated for a long period they evolve into big data. The objective of this study is to build a prognostic model, ViSiBiD, that can accurately identify dangerous clinical events of a home-monitoring patient in advance using knowledge learned from the patterns of multiple vital signs from a large number of similar patients. We developed an innovative technique that amalgamates existing data mining methods with smartly extracted features from vital sign correlations, and demonstrated its effectiveness on cloud platforms through comparative evaluations that showed its potential to become a new tool for predictive healthcare. Four clinical events are identified from 4893 patient records in publicly available databases where six bio-signals deviate from normality and different features are extracted prior to 1–2 h from 10 to 30 min observed data of those events. Known data mining algorithms along with some MapReduce implementations have been used for learning on a cloud platform. The best accuracy (95.85%) was obtained through a Random Forest classifier using all features. The encouraging learning performance using hybrid feature space proves the existence of discriminatory patterns in vital sign big data can identify severe clinical danger well ahead of time.
Keywords: Big data | Vital sign | Cloud computing | Correlations | Knowledge discovery | Data mining
مقاله انگلیسی
4 Visualizing the knowledge structure and evolution of big data research in healthcare informatics
بصری سازی ساختار دانش و تکامل تحقیقات داده های بزرگ در انفورماتیک بهداشتی-2017
Background: In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field. Methods: To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers’ production trends in the field and the trend of each paper’s co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future. Results: By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People’s Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing). Conclusion: This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions.
Keywords: Big data | Healthcare informatics | Bibliometrics | Knowledge structure | Knowledge management
مقاله انگلیسی
5 Recent Development in Big Data Analytics for Business Operations and Risk Management
توسعه اخیر در تجزیه و تحلیل داده های بزرگ برای عملیات تجاری و مدیریت ریسک-2017
“Big data” is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrialbased business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.
Index Terms: Big data analytics | business intelligence (BI) | operational risk analysis | operations management | systems relia bility and security
مقاله انگلیسی
6 Feature Selection with Annealing for Computer Vision and Big Data Learning
انتخاب ویژگی با Annealing برای بینایی ماشین و یادگیری داده های بزرگ-2017
Many computer vision and medical imaging problems are faced with learning from large-scale datasets, with millions of observations and features. In this paper we propose a novel efficient learning scheme that tightens a sparsity constraint by gradually removing variables based on a criterion and a schedule. The attractive fact that the problem size keeps dropping throughout the iterations makes it particularly suitable for big data learning. Our approach applies generically to the optimization of any differentiable loss function, and finds applications in regression, classification and ranking. The resultant algorithms build variable screening into estimation and are extremely simple to implement. We provide theoretical guarantees of convergence and selection consistency. In addition, one dimensional piecewise linear response functions are used to account for nonlinearity and a second order prior is imposed on these functions to avoid overfitting. Experiments on real and synthetic data show that the proposed method compares very well with other state of the art methods in regression, classification and ranking while being computationally very efficient and scalable.
Index Terms: Feature selection | supervised learning | regression | classification | ranking
مقاله انگلیسی
7 Optimizing End-to-End Big Data Transfers over Terabits Network Infrastructure
Optimizing End-to-End Big Data Transfers over Terabits Network Infrastructure-2017
While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today’s 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink, for instance, the data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this paper, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulk data movement framework for terabit networks, called LADS. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to benefit from hardware-level zero-copy, and operating system bypass capabilities when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and data transfer rates across the high speed networks. We also investigate the performance degradation problems of LADS due to I/O contention on the parallel file system (PFS), when multiple LADS tools share the PFS. We design and evaluate a meta-scheduler to coordinate multiple I/O streams while sharing the PFS, to minimize the I/O contention on the PFS. With our evaluations, we observe that LADS with meta-scheduling can further improve the performance by up to 14 percent relative to LADS without meta-scheduling.
Index Terms: File and storage systems | parallel file sysetms | networks | I/O scheduling
مقاله انگلیسی
8 Optimization for Speculative Execution in Big Data Processing Clusters
بهینه سازی برای اجرای احتمالی در خوشه های پردازش داده های بزرگ -2017
A big parallel processing job can be delayed substantially as long as one of its many tasks is being assigned to an unreliable or congested machine. To tackle this so-called straggler problem, most parallel processing frameworks such as MapReduce have adopted various strategies under which the system may speculatively launch additional copies of the same task if its progress is abnormally slow when extra idling resource is available. In this paper, we focus on the design of speculative execution schemes for parallel processing clusters from an optimization perspective under different loading conditions. For the lightly loaded case, we analyze and propose one cloning scheme, namely, the Smart Cloning Algorithm (SCA) which is based on maximizing the overall system utility. We also derive the workload threshold under which SCA should be used for speculative execution. For the heavily loaded case, we propose the Enhanced Speculative Execution (ESE) algorithm which is an extension of the Microsoft Mantri scheme to mitigate stragglers. Our simulation results show SCA reduces the total job flowtime, i.e., the job delay/ response time by nearly 6 percent comparing to the speculative execution strategy of Microsoft Mantri. In addition, we show that the ESE Algorithm outperforms the Mantri baseline scheme by 71 percent in terms of the job flowtime while consuming the same amount of computation resource
Index Terms: Job scheduling | speculative execution | cloning, straggler detection | optimization
مقاله انگلیسی
9 Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data
Haschedcubes: ساده، حافظه کم، اکتشاف بصری در زمان واقعی داده های بزرگ-2017
We propose Hashedcubes, a data structure that enables real-time visual exploration of large datasets that improves the state of the art by virtue of its low memory requirements, low query latencies, and implementation simplicity. In some instances, Hashedcubes notably requires two orders of magnitude less space than recent data cube visualization proposals. In this paper, we describe the algorithms to build and query Hashedcubes, and how it can drive well-known interactive visualizations such as binned scatterplots, linked histograms and heatmaps. We report memory usage, build time and query latencies for a variety of synthetic and real-world datasets, and find that although sometimes Hashedcubes offers slightly slower querying times to the state of the art, the typical query is answered fast enough to easily sustain a interaction. In datasets with hundreds of millions of elements, only about 2% of the queries take longer than 40ms. Finally, we discuss the limitations of data structure, potential spacetime tradeoffs, and future research directions.
Index Terms: Scalability | data cube |multidimensional data | interactive exploration
مقاله انگلیسی
10 Sample-Based Attribute Selective AnDE for Large Data
ande ویژگی انتخابی مبتنی بر نمونه برای داده های بزرگ-2017
More and more applications have come with large data sets in the past decade. However, existing algorithms cannot guarantee to scale well on large data. Averaged n-Dependence Estimators (AnDE) allows for flexible learning from out-of-core data, by varying the value of n (number of super parents). Hence, AnDE is especially appropriate for large data learning. In this paper, we propose a sample-based attribute selection technique for AnDE. It needs one more pass through the training data, in which a multitude of approximate AnDE models are built and efficiently assessed by leave-one-out cross validation. The use of a sample reduces the training time. Experiments on 15 large data sets demonstrate that the proposed technique significantly reduces AnDE’s error at the cost of a modest increase in training time. This efficient and scalable out-of-core approach delivers superior or comparable performance to typical in-core Bayesian network classifiers.
Index Terms: Bayesian network classifiers | large data |classification learning | attribute selection | averaged n-dependence estimators (AnDE) |leave-one-out cross validation
مقاله انگلیسی
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