Enhancing Big Data Warehousing and Analytics for Spatio-Temporal Massive Data

The increasing amount of data generated by earth observation missions like Copernicus, NASA Earth Data, and climate stations is overwhelming. Every day, terabytes of data are collected from these resources for different environment applications. Thus, this massive amount of data should be effectively managed and processed to support decision-makers. In this paper, we propose an information system-based on a low latency spatio-temporal data warehouse which aims to improve drought monitoring analytics and to support the decision-making process. The proposed framework consists of 4 main modules: (1) data collection, (2) data preprocessing, (3) data loading and storage, and (4) the visualization and interpretation module. The used data are multi-source and heterogeneous collected from various sources like remote sensing sensors, biophysical sensors, and climate sensors. Hence, this allows us to study drought in different dimensions. Experiments were carried out on a real case of drought monitoring in China between 2000 and 2020

An Overview of Methods and Tools for Extraction of Knowledge for COVID-19 from Knowledge Graphs

International audienceThe sanitary crisis provoked from the virus COVID-19 push researchers and practitioners to explore and find solutions to stamp the pandemic problem. Therefore many productions of various scientific papers and knowledge graphs are publicly accessible in internet. In this article is defined an overall description of the search engines available for COVID-19 information. A brief review of the knowledge graphs available for COVID-19 information is performed. This paper is an overview of the main relevant knowledge graph-based methods contributing in COVID-19 knowledge extraction and understanding. Furthermore, it is proposed a state-of-the-art of knowledge reasoning methods on COVID-19

Raisonnement abductif flou (théorie et pratique)

PARIS-BIUSJ-Thèses (751052125) / SudocCentre Technique Livre Ens. Sup. (774682301) / SudocPARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

Linguistic modifiers in a symbolic framework

International audienceIn common language, as well as in knowledge–based systems, the truth of a proposition can be evaluated in a qualitative manner using adverbs usually represented on a scale of symbolic degrees. To combine or aggregate such symbolic degrees, we may need scales of different precision levels. We propose to model small variations inside a degree scale using linguistic modifiers in a symbolic framework. We formally define such modifiers and we distinguish three families: reinforcing, weakening and central modifiers. We also introduce the original notion of intensity rate associated to a linguistic degree on a scale base. After that, we propose a generalization of our modifiers, so we obtain more sophisticated tools. These have been used, in particular, in an application on colorimetry that allows us to alter the color slightly. The importance of our work is that most of our linguistic modifiers verify some interesting properties on their intensity rate: notably, they assume a certain order relation

Big Remote Sensing Image Classification Based on Deep Learning Extraction Features and Distributed Spark Frameworks

Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods

Analogy and Fuzzy Interpolation in the case of Sparse Rules

International audienc