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ردیف | عنوان | نوع |
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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 |