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About 10,000 papers for “large-scale data”

Large Scale Data Streaming

MURATORE, LUCA · Electronic Theses and Dissertations Repository (University of Pisa) · 2014

Cited by 0Open access

Large-scale data analytics

Gkoulalas-Divanis, Aris, Labbi, Abderrahim · CERN Document Server (European Organization for Nuclear Research) · 2014

Cited by 0

Large Scale Data Analytics

Chung Yik Cho, Rong Kun Jason Tan, John A. Leong et al. · Studies in computational intelligence · 2019

Cited by 0

Large‐Scale Data Clustering

2008

This chapter contains sections titled: Introduction Random Sampling Methods Condensation-Based Methods Density-Based Methods Grid-Based Methods Divide and Conquer Incremental Clustering Applications Summary

Cited by 1

Large Scale Data Analysis

Janine Camille Bennett, Attila Gyulassy, Valerio Pascucci et al. · Mathematics and visualization · 2014

Cited by 0

Large-Scale Data Synthesis

Suzanne E. Pilaar Birch, Matthew H. Veres · University Press of Florida eBooks · 2022

One of the primary limiting factors in our understanding of variability across spatial and long-term temporal scales in stable isotope studies in zooarchaeology has not been lack of data but rather a lack of data exchange, integration, and synthesis. The Neotoma Database (neotomadb.org) functions as an interdisciplinary, open access database for the paleoecology community. Primary data types include proxies such as pollen, vertebrate remains, diatoms, and middens. These data are stored in various constituent databases. The recently created Faunal Isotope Database (FID) allows for the integration of stable isotope data with these existing types. Neotoma serves as a hub for storing and accessing diverse data that embody the “big data” movement and offers an unparalleled opportunity for visualizing the intersection of stable isotope analysis of zooarchaeological material with multiple other proxies for paleoecological reconstruction, ranging from the macroscopic to microscopic and biogeochemical scales. In addition to the launch and initial population of the stable isotope repository, issues that affect all so-called big data efforts exist as challenges for the future, including establishing best practices and standardization in recording/reporting, monitoring data quality, linking datasets, and increasing accessibility while protecting sensitive information.

Cited by 0

Valuing Large-Scale Data

J. Hunter Mehaffey, Ali Darehzereshki, Vinay Badhwar · The Annals of Thoracic Surgery · 2025

Cited by 0

Large Scale Data Analytics

Chung Yik Cho, Rong Kun Jason Tan, John A. Leong et al. · Studies in computational intelligence · 2019

Cited by 3

Large-Scale Data Processing

Huan Liu · 2010

Contents 5.1 Introduction ...............................................................................................90 5.2MapReduce................................................................................................93

Cited by 3

DataTrove: large scale data processing

Penedo, Guilherme, Kydlíček, Hynek, Cappelli, Alessandro et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

DataTrove is a library to process, filter and deduplicate text data at a very large scale. It provides a set of prebuilt commonly used processing blocks with a framework to easily add custom functionality. DataTrove processing pipelines are platform-agnostic, running out of the box locally or on a slurm cluster. Its (relatively) low memory usage and multiple step design makes it ideal for large workloads, such as to process an LLM's training data.

Cited by 0Open access

DataTrove: large scale data processing

Penedo, Guilherme, Kydlíček, Hynek, Cappelli, Alessandro et al. · Zenodo (CERN European Organization for Nuclear Research) · 2026

DataTrove is a library to process, filter and deduplicate text data at a very large scale. It provides a set of prebuilt commonly used processing blocks with a framework to easily add custom functionality. DataTrove processing pipelines are platform-agnostic, running out of the box locally or on a slurm cluster. Its (relatively) low memory usage and multiple step design makes it ideal for large workloads, such as to process an LLM's training data.

Cited by 1Open access

DataTrove: large scale data processing

Guilherme Penedo, Hynek Kydlíček, Alessandro Cappelli et al. · Zenodo (CERN European Organization for Nuclear Research) · 2026

DataTrove is a library to process, filter and deduplicate text data at a very large scale. It provides a set of prebuilt commonly used processing blocks with a framework to easily add custom functionality. DataTrove processing pipelines are platform-agnostic, running out of the box locally or on a slurm cluster. Its (relatively) low memory usage and multiple step design makes it ideal for large workloads, such as to process an LLM's training data.

Cited by 0Open access

Large-scale genomic data mining.

Curtis Huttenhower (21159), Oliver Hofmann (14249) · Figshare · 2015

<p>A schematic overview of possible inputs, data sources, network models, and output predictions from computational screens leveraging many genome-scale datasets. Note that both the “output” pathway model and the “input” experimental data are represented as networks: directed regulatory binding site targets, undirected weighted coexpression, and undirected interactions, respectively. As demonstrated by the sample analysis in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000779#pcbi-1000779-box001" target="_blank">Box 1</a>, biological networks provide a uniform framework within which both experimental data and predicted models can be represented, facilitating integrative analyses.</p>

Cited by 0Open access