# Data Mining & Databases — PUBLICMCP Public Library

Published articles on data mining, knowledge discovery, database systems, and large-scale data analysis from OpenAlex.

- Source institution: OpenAlex
- Set id: `openalex-set-data-mining`
- Items: 99
- Canonical page: https://publicmcp.org/library/data-mining

## Query via MCP (no API key)

```
search_library(query="data mining", source="openalex")
# endpoint: https://library.publicmcp.org/mcp — no API key required
```

## Sample items (25 of 99)

- [Advanced Modelling of Soil Organic Carbon Content in Coal Mining Areas Using Integrated Spectral Analysis: A Dengcao Coal Mine Case Study](https://doi.org/10.38124/ijisrt/ijisrt24may2382)
- [MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation](https://doi.org/10.1093/nar/gkae253)
- [ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support](https://doi.org/10.1093/nar/gkae236)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://doi.org/10.1109/cvpr52688.2022.01324)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://doi.org/10.1109/tgrs.2021.3130716)
- [ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discovery](https://doi.org/10.1021/acs.jcim.0c00675)
- [The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data](https://doi.org/10.1038/s41597-020-0534-3)
- [Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction](https://doi.org/10.1021/acscentsci.9b00576)
- [Sustainable manufacturing in Industry 4.0: an emerging research agenda](https://doi.org/10.1080/00207543.2019.1652777)
- [Crop Yield Prediction Using Deep Neural Networks](https://doi.org/10.3389/fpls.2019.00621)
- [The IllustrisTNG simulations: public data release](https://doi.org/10.1186/s40668-019-0028-x)
- [Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture](https://doi.org/10.1111/1467-8551.12355)
- [A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications](https://doi.org/10.1109/access.2019.2953499)
- [Supply chain risk management and artificial intelligence: state of the art and future research directions](https://doi.org/10.1080/00207543.2018.1530476)
- [SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates](https://doi.org/10.1103/physrevmaterials.2.083802)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://doi.org/10.1109/cvpr.2018.00132)
- [Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors](https://doi.org/10.1109/tccn.2018.2835460)
- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://doi.org/10.1109/tsg.2018.2818167)
- [Learning under Concept Drift: A Review](https://doi.org/10.1109/tkde.2018.2876857)
- [Long Short-Term Memory Network for Remaining Useful Life estimation](https://doi.org/10.1109/icphm.2017.7998311)
- [Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management](https://doi.org/10.1108/ijopm-02-2015-0078)
- [Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The <i>R</i> Package <b>lcmm</b>](https://doi.org/10.18637/jss.v078.i02)
- [An Overview and Deep Investigation on Sampled-Data-Based Event-Triggered Control and Filtering for Networked Systems](https://doi.org/10.1109/tii.2016.2607150)
- [Convolutional Matrix Factorization for Document Context-Aware Recommendation](https://doi.org/10.1145/2959100.2959165)
- [Critical analysis of Big Data challenges and analytical methods](https://doi.org/10.1016/j.jbusres.2016.08.001)

## About

Part of the PUBLICMCP Public Library — a free, open MCP server exposing 2.7M+ primary sources to AI agents. Every item is attributed and linked to its originating institution. Hub: https://publicmcp.org/library · Index: https://publicmcp.org/library/index.md · Site guide: https://publicmcp.org/llms.txt