Subtopic Deep Dive

← Big Data Technologies and Applications

Big Data in Machine Learning
Research Guide

What is Big Data in Machine Learning?

Big Data in Machine Learning applies scalable data processing techniques to train machine learning models on massive, high-volume datasets exceeding traditional computational limits.

This subtopic addresses challenges in handling volume, velocity, and variety of data for ML model training (Watson, 2014). Key methods include data reduction and distributed analytics to prevent overfitting on large datasets (Rehman et al., 2016). Over 10 papers from 2011-2023 explore applications in healthcare, disaster management, and AI systems, with foundational works cited over 300 times each.

Curated Papers

Key Challenges

Why It Matters

Big data techniques enable ML models to analyze petabyte-scale healthcare data for predictive diagnostics (Dash et al., 2019). In disaster management, they process real-time sensor streams for rapid response predictions (Yu et al., 2018). Financial and smart city applications rely on these methods for scalable AI deployment, as surveyed in explainable AI frameworks (Javed et al., 2023). Watson (2014) outlines technologies powering industry-wide analytics from social media and IoT sources.

Key Research Challenges

Scalability of Model Training

Training deep models on terabyte datasets requires distributed systems to manage volume and velocity (Watson, 2014). Current frameworks struggle with real-time processing of streaming data (Rehman et al., 2016). This limits deployment in high-stakes domains like healthcare (Dash et al., 2019).

Overfitting on Massive Data

Large datasets amplify overfitting risks despite abundant samples, demanding advanced regularization (boyd and Crawford, 2011). Data reduction methods aim to preserve signal while cutting noise (Rehman et al., 2016). Verification of model generalization remains inconsistent across big data sources.

Data Privacy and Ethics

Big data analytics raise surveillance concerns in ML applications (Degli Esposti, 2014). Ethical biases emerge in training on uncurated massive datasets (boyd and Crawford, 2011). Balancing utility with privacy compliance hinders scalable ML adoption.

Essential Papers

Big data in healthcare: management, analysis and future prospects

Sabyasachi Dash, Sushil Kumar Shakyawar, Lokesh Sharma et al. · 2019 · Journal Of Big Data · 1.6K citations

Abstract ‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Va...

ARTIFICIAL INTELLIGENCE FOR THE REAL WORLD

· 2023 · International Research Journal of Modernization in Engineering Technology and Science · 1.4K citations

Artificial intelligence (A.I.) is a multidisciplinary field aimed at automating tasks that currently need human intelligence.Despite its lack of general familiarity, artificial intelligence (AI) is...

Six Provocations for Big Data

danah boyd, Kate Crawford · 2011 · SSRN Electronic Journal · 416 citations

Big Data in Natural Disaster Management: A Review

Manzhu Yu, Chaowei Yang, Yun Li · 2018 · Geosciences · 413 citations

Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural ...

Tutorial: Big Data Analytics: Concepts, Technologies, and Applications

Hugh J. Watson · 2014 · Communications of the Association for Information Systems · 336 citations

We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social medi...

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Ekaterina Olshannikova, Aleksandr Ometov, Yevgeni Koucheryavy et al. · 2015 · Journal Of Big Data · 303 citations

This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenge...

Data ex Machina: Introduction to Big Data

David Lazer, Jason Pilny Radford · 2017 · Annual Review of Sociology · 303 citations

Social life increasingly occurs in digital environments and continues to be mediated by digital systems. Big data represents the data being generated by the digitization of social life, which we br...

Reading Guide

Foundational Papers

Start with boyd and Crawford (2011) for ethical provocations in big data, then Watson (2014) for analytics concepts and technologies applied to ML-scale data.

Recent Advances

Study Dash et al. (2019) for healthcare ML applications and Rehman et al. (2016) for data reduction methods critical to scalable training.

Core Methods

Core techniques: distributed processing (Watson, 2014), reduction via sampling (Rehman et al., 2016), and ethical analytics frameworks (boyd and Crawford, 2011).

How PapersFlow Helps You Research Big Data in Machine Learning

Discover & Search

Research Agent uses searchPapers and exaSearch to find core papers like 'Big data in healthcare: management, analysis and future prospects' by Dash et al. (2019), then citationGraph reveals 1648 citing works on scalable ML, while findSimilarPapers uncovers related reduction techniques from Rehman et al. (2016).

Analyze & Verify

Analysis Agent applies readPaperContent to extract scalability methods from Watson (2014), verifies claims with verifyResponse (CoVe) against boyd and Crawford (2011) provocations, and runs PythonAnalysis with pandas to replicate data reduction stats from Rehman et al. (2016), graded via GRADE for evidence strength.

Synthesize & Write

Synthesis Agent detects gaps in distributed ML training across Dash et al. (2019) and Yu et al. (2018), flags contradictions in ethical claims, then Writing Agent uses latexEditText, latexSyncCitations for 10+ papers, and latexCompile to generate a review section with exportMermaid diagrams of data pipelines.

Use Cases

"Analyze overfitting mitigation in big data ML training from healthcare papers"

Research Agent → searchPapers('overfitting big data ML healthcare') → Analysis Agent → runPythonAnalysis(pandas simulation of dataset reduction from Rehman et al. 2016) → statistical metrics plot showing variance reduction.

"Write a LaTeX survey section on big data analytics for disaster prediction"

Synthesis Agent → gap detection(Yu et al. 2018 + Watson 2014) → Writing Agent → latexEditText(draft) → latexSyncCitations(5 papers) → latexCompile → formatted PDF with integrated citations and figures.

"Find GitHub repos implementing big data reduction for ML from survey papers"

Research Agent → searchPapers('big data reduction ML') → Code Discovery workflow (paperExtractUrls → paperFindGithubRepo(Rehman et al. 2016) → githubRepoInspect) → list of 3 repos with code snippets for sampling methods.

Automated Workflows

Deep Research workflow scans 50+ papers via searchPapers on 'big data machine learning scalability', chains to DeepScan for 7-step verification of methods in Dash et al. (2019), producing a structured report with GRADE scores. Theorizer generates hypotheses on ethical ML scaling from boyd and Crawford (2011) + Javed et al. (2023), tested via CoVe chain-of-verification.

Try Doxa for Big Data in Machine Learning Research

Frequently Asked Questions

What defines Big Data in Machine Learning?

It involves scalable techniques for training ML models on high-volume, high-velocity datasets with variety from sources like IoT and social media (Watson, 2014).

What are key methods in this subtopic?

Methods include data reduction surveys (Rehman et al., 2016) and distributed analytics for healthcare (Dash et al., 2019), addressing volume-velocity challenges.

What are major papers?

Foundational: boyd and Crawford (2011, 416 citations); Watson (2014, 336 citations). Recent: Dash et al. (2019, 1648 citations); Javed et al. (2023, 168 citations).

What open problems exist?

Scalable real-time training without overfitting, ethical data use in surveillance-prone analytics (boyd and Crawford, 2011; Degli Esposti, 2014), and verifiable explanations in smart city ML (Javed et al., 2023).