Data Centred Intelligent Geosciences: Research Agenda and Opportunities, Position Paper

• URL: http://arxiv.org/abs/2209.02384v1
• Date: Sat, 20 Aug 2022 12:30:32 GMT
• Title: Data Centred Intelligent Geosciences: Research Agenda and Opportunities, Position Paper
• Authors: Aderson Farias do Nascimento, Martin A. Musicante, Umberto Souza da Costa, Bruno M. Carvalho, Marcus Alexandre Nunes, and Genoveva Vargas-Solar
• Abstract summary: This knowledge is produced from applying statistical modelling, Machine Learning, and modern data analytics methods on geodata collections. The problems address open methodological questions in model building, models' assessment, prediction, and forecasting.
• Score: 1.3632312903156156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes and discusses our vision to develop and reason about best practices and novel ways of curating data-centric geosciences knowledge (data, experiments, models, methods, conclusions, and interpretations). This knowledge is produced from applying statistical modelling, Machine Learning, and modern data analytics methods on geo-data collections. The problems address open methodological questions in model building, models' assessment, prediction, and forecasting workflows.

Related papers

• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• A Survey of Latent Factor Models in Recommender Systems [0.0]
  This survey systematically reviews latent factor models in recommender systems. The literature is examined through a structured framework covering learning data, model architecture, learning strategies, and optimization techniques.
  arXiv  Detail & Related papers  (2024-05-28T11:28:59Z)
• A Survey on Data Selection for Language Models [148.300726396877]
  Data selection methods aim to determine which data points to include in a training dataset. Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive. Few organizations have the resources for extensive data selection research.
  arXiv  Detail & Related papers  (2024-02-26T18:54:35Z)
• Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective [35.620874971064765]
  This work takes a "data-centric" view, examining how data collection, processing, and analysis contribute to explainable AI (XAI) We categorize existing work into three categories subject to their purposes: interpretations of deep models, influences of training data, and insights of domain knowledge. Specifically, we distill XAI methodologies into data mining operations on training and testing data across modalities.
  arXiv  Detail & Related papers  (2024-01-09T06:27:09Z)
• Information Fusion for Assistance Systems in Production Assessment [49.40442046458756]
  We provide a framework for the fusion of n number of information sources using the evidence theory. We provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach.
  arXiv  Detail & Related papers  (2023-08-31T22:08:01Z)
• Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science [10.794642538442107]
  We describe how the concepts of positionality and reflexivity can be adapted to provide a framework for understanding data science work. We describe the challenges of adapting these concepts for data science work and offer annotator fingerprinting and position mining as promising solutions.
  arXiv  Detail & Related papers  (2022-03-08T16:02:03Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• Geostatistical Learning: Challenges and Opportunities [0.0]
  We introduce the geostatistical (transfer) learning problem, and illustrate the challenges of learning from geospatial data. Experiments with synthetic Gaussian process data as well as with real data from geophysical surveys in New Zealand indicate that none of the methods are adequate for model selection in a geospatial context.
  arXiv  Detail & Related papers  (2021-02-17T14:33:15Z)
• Data and its (dis)contents: A survey of dataset development and use in machine learning research [11.042648980854487]
  We survey the many concerns raised about the way we collect and use data in machine learning. We advocate that a more cautious and thorough understanding of data is necessary to address several of the practical and ethical issues of the field.
  arXiv  Detail & Related papers  (2020-12-09T22:13:13Z)
• A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs [77.34726150561087]
  We survey the current research landscape on word, sentence and knowledge graph embedding algorithms. We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research.
  arXiv  Detail & Related papers  (2020-10-26T16:08:13Z)
• A Survey on Data Pricing: from Economics to Data Science [61.72030615854597]
  We examine various motivations behind data pricing and understand the economics of data pricing. We discuss both digital products and data products. We consider a series of challenges and directions for future work.
  arXiv  Detail & Related papers  (2020-09-09T19:31:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.