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Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
[article]
2019
arXiv
pre-print
Preserved Fulltext
We identify dependencies among the data preprocessing techniques and propose MLClean, a unified data cleaning framework that integrates the techniques and helps train accurate and fair models. ...
This work is part of a broader trend of Big data -- Artificial Intelligence (AI) integration. ...
From a data management standpoint, we contend that it is a good time to extend the data cleaning problem for the pressing needs of modern machine learning for accurate, fair, and robust model training. ...
arXiv:1904.10761v1
fatcat:fcvusaqesndyxlnnndleu4pkh4
A Survey on Data Collection for Machine Learning: a Big Data – AI Integration Perspective
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1811.03402v1 fatcat:m6pgbchxabctbbq62f5jelsetq
The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research ...
Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. ...
In the future, we expect the integration of Big data and AI to happen not only in data collection, but in all aspects of machine learning. ...
arXiv:1811.03402v2
fatcat:wviufzo2p5dtrnfrbisgkzrpd4
Data Economy 2.0: From Big Data Value to AI Value and a European Data Space
[chapter]
2021
The Elements of Big Data Value
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The chapter describes the European AI framework as a foundation for deploying AI successfully and the critical need for a common European data space to power this vision. ...
This chapter explores the opportunities and challenges of big data and AI in exploiting data ecosystems and creating AI value. ...
These tools, methods and processes integrate AI, Data and Robotics technologies into systems and are responsible for ensuring that core system properties and characteristics such as safety, robustness, ...
doi:10.1007/978-3-030-68176-0_16
fatcat:n7fgc76zbfbznm4npmt3iis7si
Big Continuous Data: Dealing with Velocity by Composing Event Streams
[chapter]
2016
Big Data Concepts, Theories, and Applications
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Nevertheless, to the best of our knowledge, few approaches integrate different composition techniques (online and post-mortem) for dealing with Big Data velocity. ...
Event streams with their volume and continuous production cope mainly with two of the characteristics given to Big Data by the 5V's model: volume & velocity. ...
Nevertheless, to the best of our knowledge, few approaches integrate different composition techniques (online and post-mortem) for dealing with Big Data velocity and volume. ...
doi:10.1007/978-3-319-27763-9_1
fatcat:vk67le6wnjbd5cdztvhq5i3dra
Manufacturing process data analysis pipelines: a requirements analysis and survey
2019
Journal of Big Data
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AI thanks G. Gridling and R. Trubko for their helpful discussions. ...
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ...
Thus, there is a strong need for versatile and well-integrated controls for platform-wide data governance and policy enforcement. ...
doi:10.1186/s40537-018-0162-3
fatcat:6tlovbsubzhqfagjjyimsythm4
Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review
[article]
2020
arXiv
pre-print
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the quality for big data applications include correctness, performance, availability, scalability, reliability and so on; 3) the existing QA technologies, including analysis, specification, model-driven ...
This study provides a solid foundation for research on QA technologies of big data applications. However, many challenges of big data applications regarding quality still remain. ...
Paper P4 proposes an approach which integrates Architecture Analysis & Design Language (AADL) to consider big data properties through customized concepts and models in a rigorous way. ...
arXiv:2002.01759v2
fatcat:k6fdzjfsujbxbo7pegh6qtz5oe
A study of data analytics and applications in multiple field using big data and internet of things(IoT)
2021
Zenodo
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A study of data analytics and applications in multiple field using big data and internet of things(IoT): PDF ...
In the process of making accurate decisions based on
facts and statistics, Islamic fund managers have
recently begun integrating AI and big data analytics
into their approach, removing any stereotypes ...
Data analysts and data engineers work together in the process of data analytics to collect, integrate, and prepare data for analytical model creation, testing, and revision, ensuring accurate performance ...
doi:10.5281/zenodo.5148785
fatcat:ysqdqumwgbfr7kvbpbrd4n6vr4
Survey on data analysis in social media: A practical application aspect
2020
Big Data Mining and Analytics
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It serves as a critical information source with large volumes, high velocity, and a wide variety of data. ...
data. ...
Data collection Most social media platforms, such as Twitter and Facebook, provide robust official API for developers to collect data. ...
doi:10.26599/bdma.2020.9020006
fatcat:msf6yz7tozbdne2mutwepo2ujy
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities
2021
IEEE Access
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Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. ...
The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities ...
Finally, we designed a reference model for digital twinning that exploits IoT, big data, and AI-ML approaches. The rest of the paper is organized as follows. ...
doi:10.1109/access.2021.3060863
fatcat:cvm5ubwwrbcdph5z37dvdodgx4
Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review
2020
Applied Sciences
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This study provides a solid foundation for research on QA technologies of big data applications and can help developers of big data applications apply suitable QA technologies. ...
We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. ...
Metamodels and model mapping approaches for other kinds of big data applications are also urgently needed. ...
doi:10.3390/app10228052
fatcat:yqutaywdafduxhlnlm74pps6r4
Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research
2016
Technological forecasting & social change
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The resulting knowledge may hold interest for R&D management and science policy in practice. ...
An empirical case study of Awards data from the United States National Science Foundation, Division of Computer and Communication Foundation, is performed to demonstrate the proposed method. ...
Introduction The coming of the Big Data Age introduces big opportunities and big challenges for modern society. ...
doi:10.1016/j.techfore.2016.01.015
fatcat:zjqqq2pwondftgvl6zjbpq6c2y
A Roadmap For Big Data Incorporating Both The Research Roadmap And The Policy Roadmap: Byte Policy And Research Roadmap
2017
Zenodo
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A roadmap for big data incorporating both the research roadmap and the policy roadmap: BYTE Policy and Research Roadmap. Deliverable D6.1 BYTE Project ...
There is a need of better integration between algorithmic and human computation approaches (Freitas and Curry 2016) . ...
The policy roadmap focuses on safeguarding the conditions for the creation of a European big data infrastructure while promoting social good and ensuring a fair data governance. ...
doi:10.5281/zenodo.1195744
fatcat:aqpyxix3crawhpheliz3v33kye
A Roadmap for Big Model
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2203.14101v3 fatcat:m7pyxdvamfdndaipb3lew3syhyOther Versions
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. ...
We introduce 16 specific BM-related topics in those four parts, they are Data, Knowledge, Computing System, Parallel Training System, Language Model, Vision Model, Multi-modal Model, Theory&Interpretability ...
The goal also needs to ensure the security of the big model's underlying data, the interpretation and fairness of algorithms, and the robustness and accountability of the model. AI FOR GOOD. ...
arXiv:2203.14101v4
fatcat:rdikzudoezak5b36cf6hhne5u4
Selectivity Estimation with Deep Likelihood Models
[article]
2019
arXiv
pre-print
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However, direct application of these models leads to a limited estimator that is prohibitively expensive to evaluate for range and wildcard predicates. ...
To make a truly usable estimator, we develop a Monte Carlo integration scheme on top of likelihood models that can efficiently handle range queries with dozens of filters or more. ...
Outlier detection or data cleaning can benefit from a statistical model to check how likely a tuple is dirty [18] (i.e., outside the data distribution). ...
arXiv:1905.04278v1
fatcat:ls6n36rjyrge3jqhs4jofwvnrq
Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing
2021
Frontiers in Artificial Intelligence
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Overall, such a customizable GCRISP-DS framework provides an enhancement for model improvements and reusability by minimizing robustness-issues. ...
However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data- and model development-related issues. ...
ROBUSTNESS ISSUES OF ML AND AI MODELS In the following, we discuss data-related issues for robust data analytics because the shortcomings of data reflect on model evaluation and on the deployment phase ...
doi:10.3389/frai.2021.576892
fatcat:zyf6bk2mhvd2fnoy7hpdhoagtu
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