Development of Semantics-Based Distributed Middleware for Heterogeneous Data Integration and its Application for Drought
- URL: http://arxiv.org/abs/2405.10713v1
- Date: Fri, 17 May 2024 11:44:22 GMT
- Title: Development of Semantics-Based Distributed Middleware for Heterogeneous Data Integration and its Application for Drought
- Authors: A Akanbi,
- Abstract summary: Drought is a complex environmental phenomenon that affects millions of people and communities all over the globe.
This research develops a semantics-based data integration that encompasses and integrates data models of local indigenous knowledge and sensor data.
The local indigenous knowledge on drought gathered from the domain experts is transformed into rules to be used for performing deductive inference.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Drought is a complex environmental phenomenon that affects millions of people and communities all over the globe and is too elusive to be accurately predicted. This is mostly due to the scalability and variability of the web of environmental parameters that directly/indirectly causes the onset of different categories of drought. Since the dawn of man, efforts have been made to uniquely understand the natural indicators that provide signs of likely environmental events. These indicators/signs in the form of indigenous knowledge system have been used for generations. The intricate complexity of drought has, however, always been a major stumbling block for accurate drought prediction and forecasting systems. Recently, scientists in the field of agriculture and environmental monitoring have been discussing the integration of indigenous knowledge and scientific knowledge for a more accurate environmental forecasting system in order to incorporate diverse environmental information for a reliable drought forecast. Hence, in this research, the core objective is the development of a semantics-based data integration middleware that encompasses and integrates heterogeneous data models of local indigenous knowledge and sensor data towards an accurate drought forecasting system for the study areas. The local indigenous knowledge on drought gathered from the domain experts is transformed into rules to be used for performing deductive inference in conjunction with sensors data for determining the onset of drought through an automated inference generation module of the middleware. The semantic middleware incorporates, inter alia, a distributed architecture that consists of a streaming data processing engine based on Apache Kafka for real-time stream processing; a rule-based reasoning module; an ontology module for semantic representation of the knowledge bases.
Related papers
- Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction [10.646376827353551]
Multi-source spatial point data prediction is crucial in fields like environmental monitoring and natural resource management.
Existing models in this area often fall short due to their domain-specific nature and lack a strategy for integrating information from various sources.
We introduce an innovative multi-source spatial point data prediction framework that adeptly aligns information from varied sources without relying on ground truth labels.
arXiv Detail & Related papers (2024-06-30T16:13:13Z) - Domain Adaptation for Sustainable Soil Management using Causal and
Contrastive Constraint Minimization [13.436399861462323]
We propose a multi-modal, scalable framework that can estimate organic matter from remote sensing data.
We leverage inherent structure in the data and train the model to discriminate among domains using contrastive learning.
We shed light on the interpretability of the framework by identifying attributes that are important for improving generalization.
arXiv Detail & Related papers (2024-01-13T23:51:42Z) - FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems [28.166089112650926]
FREE maps available environmental data into a text space and then converts the traditional predictive modeling task in environmental science to the semantic recognition problem.
When used for long-term prediction, FREE has the flexibility to incorporate newly collected observations to enhance future prediction.
The efficacy of FREE is evaluated in the context of two societally important real-world applications, predicting stream water temperature in the Delaware River Basin and predicting annual corn yield in Illinois and Iowa.
arXiv Detail & Related papers (2023-11-17T00:53:09Z) - Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic
Forecasting [47.1051445072085]
We argue to rethink the sensor's dependency modeling from two hierarchies: regional and global.
We generate representative and common-temporal patterns as global nodes to reflect a global dependency between sensors.
In pursuit of the generality of reality of node representations, we incorporate a Meta GCN to propagate and global nodes in the physical data space.
arXiv Detail & Related papers (2023-09-20T13:08:34Z) - Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach.
Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
arXiv Detail & Related papers (2023-09-12T13:28:06Z) - Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User
Experiences [118.91584633024907]
A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
arXiv Detail & Related papers (2023-04-29T00:39:50Z) - Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
Particles for Frontier Exploration [55.41644538483948]
This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles.
It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format.
The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
arXiv Detail & Related papers (2023-04-27T20:21:18Z) - Change Detection for Local Explainability in Evolving Data Streams [72.4816340552763]
Local feature attribution methods have become a popular technique for post-hoc and model-agnostic explanations.
It is often unclear how local attributions behave in realistic, constantly evolving settings such as streaming and online applications.
We present CDLEEDS, a flexible and model-agnostic framework for detecting local change and concept drift.
arXiv Detail & Related papers (2022-09-06T18:38:34Z) - ESTemd: A Distributed Processing Framework for Environmental Monitoring
based on Apache Kafka Streaming Engine [0.0]
Distributed networks and real-time systems are becoming the most important components for the new computer age, the Internet of Things.
Data generated offers the ability to measure, infer and understand environmental indicators, from delicate ecologies to natural resources to urban environments.
We propose a distributed framework Event STream Processing Engine for Environmental Monitoring Domain (ESTemd) for the application of stream processing on heterogeneous environmental data.
arXiv Detail & Related papers (2021-04-02T15:04:15Z) - A Data Scientist's Guide to Streamflow Prediction [55.22219308265945]
We focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow.
This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way.
arXiv Detail & Related papers (2020-06-05T08:04:37Z)
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.