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Towards Geo-Distributed Machine Learning [article]

Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola
2016 arXiv   pre-print
These types of applications form a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML).  ...  The resulting data is "born" geographically distributed. On the other hand, many machine learning applications require a global view of such data in order to achieve the best results.  ...  These types of applications that deal with geo-distributed datasets belong to a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML).  ... 
arXiv:1603.09035v1 fatcat:skmpf6odwzemdick6oncj6wnoe

Open Source Science for Large-Scale Data Mining in Earth Observation

Conrad M Albrecht
2022 Zenodo  
The presentation shares to the NASA Earth System Observatory team DLR's expertise on the design of open-source-based big geo-spatial data processing platforms for remote sensing AI workloads in the context  ...  ■ efficient data assembly of co-registered spatio-temporal geo-information for machine learning ■ co-location of data and compute for Big Geo-Data processing ■ geo-data discovery and cross-layer operations  ...  Large-Scale Data Mining in Earth Observation*○ a key challenge for deep/machine learning in Earth observation is limited availability of labels for model training due to the human labor-intensive collection  ... 
doi:10.5281/zenodo.6315121 fatcat:mxo2pc23jzfldkahu5oz4xnx2m

Towards Scalable Geospatial Remote Sensing for Efficient OSM Labeling

Rui Zhang, Marcus Freitag, Conrad Albrecht, Wei Zhang, Siyuan Lu
2019 Zenodo  
Towards Scalable Geospatial Remote Sensing for Efficient OSM Labeling In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.).  ...  Our work discusses and demonstrates how to link tools from big data analytics and machine learning to geo-spatial datasets at scale in order to extract value from openly available spatio-temporal datasets  ...  For example, projects for distributed non-relational database systems such as HBase ( ) or in-memory distributed compute frameworks such as Spark are available to run on commodity  ... 
doi:10.5281/zenodo.3387714 fatcat:zag2m6lmnfdxtjigq6baqnlv6a

ALE: automated label extraction from GEO metadata

Cory B. Giles, Chase A. Brown, Michael Ripperger, Zane Dennis, Xiavan Roopnarinesingh, Hunter Porter, Aleksandra Perz, Jonathan D. Wren
2017 BMC Bioinformatics  
Conclusion: Here we present an automated method to extract labels for age, gender, and tissue from textual metadata and GEO data using both a heuristic approach as well as machine learning.  ...  We then use machine-learning methods to predict labels, based upon gene expression of the samples and compare this to the text-based method.  ...  This choice allows for more certainty in the results, which are used as training data provided to the GEO expression machine learning algorithm.  ... 
doi:10.1186/s12859-017-1888-1 pmid:29297276 pmcid:PMC5751806 fatcat:s5oihdfauzeclfa6ebpm2a2mnq

A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management [article]

Ninad Hogade, Sudeep Pasricha
2022 arXiv   pre-print
data-driven and Machine Learning (ML) based alternatives.  ...  But these techniques are difficult to scale to geo-distributed problem sizes and have limited applicability in dynamic heterogeneous system environments, forcing cloud service providers to explore intelligent  ...  Hogade I OVERVIEW OF MACHINE LEARNING METHODS Machine Learning (ML) refers to a machine's ability to learn from data.  ... 
arXiv:2205.08072v1 fatcat:nz3vvmdrard6hgydagnsadrnpm

2020 Index IEEE Transactions on Cloud Computing Vol. 8

2021 IEEE Transactions on Cloud Computing  
Predicting Workflow Task Execution Time in the Cloud Using A Two-Stage Machine Learning Approach. Pham, T., +, TCC Jan.  ...  Revenue Maximization for Dynamic Expansion of Geo-Distributed Cloud Data Centers.  ...  Frequency control Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing. Lucanin, D., +, TCC Oct.-Dec. 2020  ... 
doi:10.1109/tcc.2021.3055041 fatcat:nppinqsievad3gr42ppehwp7yi

Using Data-Mining Technique for Census Analysis to Give GeoSpatial Distribution of Nigeria

2013 IOSR Journal of Computer Engineering  
distribution.  ...  This paper is an effort towards harnessing the power of data-mining technique to develop mining model applicable to the analysis of census data that could uncover some hidden patterns to get their geo-spatial  ...  The roots of data-mining originate in three areas, Classical statistics, artificial intelligence and machine learning.  ... 
doi:10.9790/0661-1420105 fatcat:2hdzige7ofgpndanx5ta637juu

Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics

Michael Hughes, John Kodros, Jeffrey Pierce, Matthew West, Nicole Riemer
2018 Atmosphere  
This framework demonstrates how machine learning can be applied to bridge the gap between detailed process modeling and a large-scale climate model.  ...  We used the output of a large ensemble of particle-resolved box model simulations in conjunction with machine learning techniques to train a model of the mixing state metric χ.  ...  Figure 7 . 7 Global distribution of size-resolved χ values from the machine-learning model based GEOS-Chem-TOMAS inputs for the months of: January (top); and July (bottom).  ... 
doi:10.3390/atmos9010015 fatcat:p3so2rc35jbojmzajh7rxl5f5i


D. Espinoza Molina, K. Alonso, M. Datcu
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Image processing together with machine learning methods, relevance feedback techniques, and human expertise are used to annotate the image content into a land use land cover catalogue.  ...  All the generated information is stored into a geo-database supporting the link between different types of information and the computation of queries and analytics.  ...  Semantic definition via machine learning methods: When all the generated information is available in the geo-database a new process starts: the semantic definition by using the support vector machine and  ... 
doi:10.5194/isprsarchives-xl-1-w5-185-2015 fatcat:egonz6z53bdx3ldhasy64ea3ku

Predicting Loss Risks for B2B Tendering Processes [article]

Eelaaf Zahid, Yuya Jeremy Ong, Aly Megahed, Taiga Nakamura
2021 arXiv   pre-print
Here we also define M (W ), where we train a machine learning model M with respect to the sample weights W .  ...  CONCLUSION In this paper, we presented a novel machine learning model that performed multi-classification for win prediction.  ... 
arXiv:2109.06815v1 fatcat:44g62bmmqrbadc2hlcmkjshr6q

Table of Contents

2020 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)  
Clouds DisGB: Using Geo-Context Information for Efficient Routing in Geo-Distributed Pub/Sub Systems 67 Jonathan Hasenburg (Technische Universität Berlin) and David Bermbach (Technische Universität  ...  Bittencourt (University of Campinas, Brazil) Robust Resource Scaling of Containerized Microservices with Probabilistic Machine Learning 122 Peng Kang (University of Texas at San Antonio) and Palden  ... 
doi:10.1109/ucc48980.2020.00004 fatcat:4iq4eh53nnb65f3whbobxcemiq

Wikiwhere: An interactive tool for studying the geographical provenance of Wikipedia references [article]

Martin Körner, Tatiana Sennikova, Florian Windhäuser, Claudia Wagner, Fabian Flöck
2016 arXiv   pre-print
Instead of relying solely on the IP location of a given URL our machine learning models consider several features.  ...  Such patterns could be an indicator of bias towards certain national contexts when referencing facts and statements in Wikipedia.  ...  To investigate this suspicion, we set up a machine learning model to infer geoprovenance.  ... 
arXiv:1612.00985v2 fatcat:efci33enbvgevpdv7w6syhlkz4

Bayesian Selection Of Grammar Productions For The Language Of Thought [article]

Sergio Romano, Alejo Salles, Marie Amalric, Stanislas Dehaene, Mariano Sigman, Santiago Figueria
2017 bioRxiv   pre-print
Probabilistic proposals of Language of Thoughts (LoTs) can explain learning across different domains as statistical inference over a compositionally structured hypothesis space.  ...  This constitutes a first step towards a formal link between probability and complexity modeling frameworks for LoTs.  ...  Testing the Coding Theorem for Geo Despite the fact that P M and K M are defined over a Turing Machine M, the reader should note that a LoT is not usually formalized with a Turing Machine, but instead  ... 
doi:10.1101/141358 fatcat:ty4rfls7ancgpb4jgl22dcibba

FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data [article]

Mike He Zhu, Léna Néhale Ezzine, Dianbo Liu, Yoshua Bengio
2022 arXiv   pre-print
Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed  ...  distribution is non-i.i.d. with respect to the training domains.  ...  Introduction Federated learning is a machine learning paradigm where the local clients collaboratively train a model under the orchestration of a central server (1; 2; 3).  ... 
arXiv:2205.09305v1 fatcat:g5luxwxewrf7heodzm3jkj2lzm

Toward interactive search in remote sensing imagery

Reid Porter, Don Hush, Neal Harvey, James Theiler, John F. Buford, Gabriel Jakobson, John Erickson, William J. Tolone, William Ribarsky
2010 Cyber Security, Situation Management, and Impact Assessment II; and Visual Analytics for Homeland Defense and Security II  
By contrast, the vast majority of algorithms developed in machine learning aim to replace human users in data exploitation.  ...  In this paper we describe a recently introduced machine learning problem, called rare category detection, which may be a better match to visual analytic environments.  ...  BROAD-AREA SEARCH IN REMOTE SENSING MAGERY One application where interactive machine learning will be particularly useful is broad-area search of geo-spatial imagery .  ... 
doi:10.1117/12.850787 fatcat:gdbtc5fhiffjldoqa5ejdfyaju
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