Mapping the Collaborative Platform Economy Business Practice

This study aims to develop a typological configuration that characterizes the full spectrum of collaborative platform economy business practice in the real world. The analysis is conducted on the basis of a large-scale data set which contains information on 1,335 representative platforms in more than 60 countries on five continents, covering almost all collaborative platform economy business practices mentioned in academic journals and public media. Leveraging the k-means clustering method, an empirical typology comprising seven categories of collaborative platform economy business practice is proposed: collaborative support platform, resource supply platform, authentic C2C platform, C2C mutualized mobility platform, hybrid service platform, B2C service platforms, collaborative finance platform. In addition, with the help of operating status data of the collaborative platform economy, a cross-comparative analysis was also carried out on the category differences and geographic differences.

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Large-Scale Data Learning Method for Anomaly Detection using Machine Learning for Monitoring Vibration in Vehicle Equipment

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.140.480 ◽

2020 ◽

Vol 140 (6) ◽

pp. 480-487

Author(s):

Minoru Kondo

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Large Scale ◽

Learning Method ◽

Large Scale Data ◽

Scale Data

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Faculty Opinions recommendation of Comparative assessment of large-scale data sets of protein-protein interactions.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1006598.82257 ◽

2002 ◽

Author(s):

Rob Russell

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Comparative Assessment ◽

Data Sets ◽

Protein Protein Interactions ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets

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ProGen:Provenance database generator for large-scale data set

Journal of Computer Applications ◽

10.3724/sp.j.1087.2008.02737 ◽

2009 ◽

Vol 28 (11) ◽

pp. 2737-2740

Author(s):

Xiao ZHANG ◽

Shan WANG ◽

Na LIAN

Keyword(s):

Large Scale ◽

Data Set ◽

Large Scale Data ◽

Scale Data

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Construction of integrated particle rendering environment for large scale data visualization

Impact ◽

10.21820/23987073.2018.11.9 ◽

2018 ◽

Vol 2018 (11) ◽

pp. 9-11

Author(s):

Koji Koyamada

Keyword(s):

Data Visualization ◽

Large Scale ◽

Large Scale Data ◽

Scale Data

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COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

10.1130/abs/2016am-286533 ◽

2016 ◽

Author(s):

John W. Williams ◽

◽

Simon Goring ◽

Eric Grimm ◽

Jason McLachlan

Keyword(s):

Data Model ◽

Large Scale ◽

Large Scale Data ◽

Scale Data

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A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code

SLEEP ◽

10.1093/sleep/zsaa170 ◽

2020 ◽

Author(s):

Luca Menghini ◽

Nicola Cellini ◽

Aimee Goldstone ◽

Fiona C Baker ◽

Massimiliano de Zambotti

Keyword(s):

Open Source ◽

Validation Studies ◽

Large Scale ◽

Analytical Framework ◽

Clinical Settings ◽

Large Scale Data ◽

Fast Pace ◽

Epoch Analysis ◽

Tracking Devices

Abstract Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland–Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.

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Local and global approaches of affinity propagation clustering for large scale data

Journal of Zhejiang University SCIENCE A ◽

10.1631/jzus.a0720058 ◽

2008 ◽

Vol 9 (10) ◽

pp. 1373-1381 ◽

Cited By ~ 28

Author(s):

Ding-yin Xia ◽

Fei Wu ◽

Xu-qing Zhang ◽

Yue-ting Zhuang

Keyword(s):

Large Scale ◽

Affinity Propagation ◽

Large Scale Data ◽

Affinity Propagation Clustering ◽

Scale Data

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Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽

10.1093/gigascience/giaa133 ◽

2020 ◽

Vol 9 (12) ◽

Author(s):

Ariel Rokem ◽

Kendrick Kay

Keyword(s):

Ridge Regression ◽

Large Scale ◽

Imaging Data ◽

Regularization Technique ◽

Large Scale Data ◽

Novel Approach ◽

Manual Exploration ◽

L2 Norm ◽

Software Implementations ◽

Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

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