Multi-source Heterogeneous Data Research Articles

An interoperable software system to store, associate, visualize, and publish global open science data of earth surface system

Multi-source heterogeneous, multi-modal, and multi-type open scientific data (e.g., thematic sharing sites, metadata, journal articles, etc.) on earth surface systems (EES) provide important data sources for knowledge mining, discovery, and accurate recommendations, and also pose increasing challenges, resulting in the need to develop appropriate tools to address these challenges and support decision-making. This paper constructs an interoperable software system to store, visualize, and publish open science data of ESS. Utilizing an open scientific data catalogue repository encompassing EES information as foundational input and employing an integrated modeling methodology, this system endeavors to synthesize heterogeneous surface data of diverse linguistic, sourced, and typological origins. The objective is to facilitate multidimensional data retrieval and precise data auto-recommendation, thereby fostering the dissemination of scientific data and facilitating value-added services within EES domain. The tool may be used by stakeholders including researchers, data analysts, policymakers and national authorities to support decision-making on questions ranging from locating the location of open data related to the topic, to discovering high-quality data, selecting the data with the better overall evaluation. Along with a description of the system/platform design process, its structure, and the constituent models, key results are presented relating to the user interface, and several application examples. Software systems can help modelers to use the best features of a single software tool to answer open scientific data-related questions that seek to discovery, use, comparison or synthesis within or across topics of ESS.

Towards Few-Label Vertical Federated Learning

Federated Learning (FL) provided a novel paradigm for privacy-preserving machine learning, enabling multiple clients to collaborate on model training without sharing private data. To handle multi-source heterogeneous data, vertical federated learning (VFL) has been extensively investigated. However, in the context of VFL, the label information tends to be kept in one authoritative client and is very limited. This poses two challenges for model training in the VFL scenario: On the one hand, a small number of labels cannot guarantee to train a well VFL model with informative network parameters, resulting in unclear boundaries for classification decisions; On the other hand, the large amount of unlabeled data is dominant and should not be discounted, and it’s worthwhile to focus on how to leverage them to improve representation modeling capabilities. In order to address the above two challenges, Firstly, we introduce supervised contrastive loss to enhance the intra-class aggregation and inter-class estrangement, which is to deeply explore label information and improve the effectiveness of downstream classification tasks. Secondly, for unlabeled data, we introduce a pseudo-label-guided consistency mechanism to induce the classification results coherent across clients, which allows the representations learned by local networks to absorb the knowledge from other clients, and alleviates the disagreement between different clients for classification tasks. We conduct sufficient experiments on four commonly used datasets, and the experimental results demonstrate that our method is superior to the state-of-the-art methods, especially in the low-label rate scenario, and the improvement becomes more significant.

Self-Supervised Learning and Multisource Heterogeneous Information Fusion Based Quality Anomaly Detection for Heavy-Plate Shape

Plate shape quality anomaly detection suffers from the problems of less labeled and multisource heterogeneous (MSH) data due to the complex production process with multi-process and multi-equipment. To address these problems, this paper proposes a self-supervised learning framework based on MSH contrastive learning (MSH-CL). In the proposed framework, an encoder is designed to extract the MSH information, which contains an MSH feature extract network (MSH-FEN) and an MSH feature fusion network (MSH-FFN). In MSH-FEN, a two-pathway CNN network is employed to extract the MSH feature. Then, a cross-MSH (CMSH) data fusion strategy based on the attention mechanism is designed to characterize the relationship between the MSH data in the MSH-FFN. The learning process consists of two phases, namely the self-supervised feature learning phase based on the contrastive learning (CL) and the supervised fine-tuning phase. The former phase is responsible for training the encoder with massive unlabeled data. Then, a classifier followed by the encoder is built with less labeled data in the supervised fine-tuning phase, which is applied to plate shape quality anomaly detection. Finally, the experiments are carried out on the real-world data set collected from a heavy-plate production process. The experimental results demonstrate the effectiveness and superiority of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper focuses on the problem of shape quality anomaly detection for the heavy-plate production process, which is crucial to steel-making. However, the most quality-relevant data-driven modeling approaches suffer from the problem of requiring massive labeled data. In addition, the data generated in the practice heavy-plate production process have the characteristics of multisource heterogeneous (MSH). Motivated by the challenge of the less labeled and MSH data modeling, this paper proposed a self-supervised feature learning approach to learn a feature encoder to model the MSH data. Then, fine-tuning is used to obtain the model to detect the plate shape quality. In the feature encoder, a cross-MSH (CMSH) data fusion strategy is proposed. Finally, the effectiveness of the proposed approach is demonstrated by comparing it with other advanced algorithms. The proposed approach can be extended to other heavy-plate quality anomaly detection problems with less labeled and MSH data.

Multidimensional visualization analysis based on large-scale GNSS data

Abstract With the deployment of global navigation systems such as GPS, GLONASS, GALILEO, and BeiDou, and the construction of the global observation network, global navigation satellite system (GNSS) data have greatly expanded. Traditional relational databases and file systems can no longer efficiently store and process GNSS data. In addition, multidimensional, in-depth visual analysis methods for such data are lacking. In this paper, we provide a solution and implement a GNSS data visualization system based on MapV and cesium running on the Hadoop platform, which can effectively solve the problems of the lack of storage and computing resources for these massive multisource heterogeneous data. The proposed system provides multidimensional presentations to show how the space and ground segments of the entire system are organized by analyzing GNSS data.

Research on charging demands of commercial electric vehicles based on Voronoi diagram and spatial econometrics model: An empirical study in Chongqing China

In order to address the mismatch between the current charging demand of electric vehicles and the layout of charging stations, this paper collects multi-source heterogeneous data that may affect charging demand, and explores their spatiotemporal distributions in depth. Then, this paper proposes a Voronoi polygon spatial partitioning method based on charging demand clustering and two spatial econometric models, spatial lag model (SLM) and spatial error model (SEM), to quantitatively analyze the influences of various elements on charging demands. Empirical research in Chongqing, China reveals that the spatiotemporal distribution of charging demand is uneven, with peak areas and periods for charging, and the division of Voronoi polygon can better reflect the spatial heterogeneity of charging demands than regular grid division. The positive factors include the low SOC, parking lot density, population density, and road network density, especially the low SOC and parking lot density, and the negative items contain consumption related Point Of Interest (POI), tourism related POI, and transportation hub related POI. Among them, population density and road network density, as static data, have similar influences on charging demands at different intervals, while the impact of other factors on charging demand varies with periods, with obvious peak-valley characteristics. There is spatial dependence and lag effect between charging demands within polygons, causing SLM outperforms SEM. The research results are beneficial for the rational siting and optimization of charging stations.

Evidential reasoning and lightweight multi-source heterogeneous data fusion-driven fire danger level dynamic assessment technique

Dynamic fire assessment under many uncertainties make danger level difficult to be evaluated reasonably. This investigation aims to establish a fire danger level dynamic assessment technique, which can be utilized to achieve automatically rules generation of fire assessment and multiple attributes-based fire danger level determination. In the technique, image segmentation with particle swarm optimization is used to generate the rules of fire assessment related to image type data automatically. Evidential reasoning with particle swarm optimization is used to generate the rules of fire assessment related to numeric type data automatically. Then, final fire danger level can be evaluated together with the help of the multi-source heterogeneous data. The main novelty of this investigation is that the developed multiple attributes-based assessment technique is not limited to the specific fire scenario, which is without resort to professional knowledges for determining the rules of fire assessment and training based on mass data in advance. In addition, the results of the case study of a tunnel fire support the ability of the technique, which can be used to achieve the reasonable fire danger level dynamic assessment with only the monitored data at the current time condition.

Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data

The stable operation of the process industrial system, which is integrated with various complex equipment, is the premise of production, which requires the condition monitoring and diagnosis of the system. Recently, the continuous development of deep learning (DL) has promoted the research of intelligent diagnosis in process industry systems, and the sensor system layout has provided sufficient data foundation for this task. However, these DL-driven approaches have had some shortcomings: (1) the output signals of heterogeneous sensing systems existing in process industry systems are often high-dimensional coupled and (2) the fault diagnosis model built from pure data lacks systematic process knowledge, resulting in inaccurate fitting. To solve these problems, a graph feature fusion-driven fault diagnosis of complex process industry systems is proposed in this paper. First, according to the system’s prior knowledge and data characteristics, the original multisource heterogeneous data are divided into two categories. On this basis, the two kinds of data are converted to physical space graphs (PSG) and process knowledge graphs (PKG), respectively, according to the physical space layout and reaction mechanism of the system. Second, the node features and system spatial features of the subgraphs are extracted by the graph convolutional neural network at the same time, and the fault representation information of the subgraph is mined. Finally, the attention mechanism is used to fuse the learned subgraph features getting the global-graph representation for fault diagnosis. Two publicly available process chemistry datasets validate the effectiveness of the proposed method.

How to measure and control indoor air quality based on intelligent digital twin platforms: A case study in China

In order to address the current disparity between indoor air quality assessment, prediction, and control, this study proposes an intelligent management approach for indoor air quality based on digital twin platforms. By collecting and fusing multi-level, and multi-dimensional data at the physical level, a comprehensive data integration and analysis platform is established in the digital realm, encompassing the ‘Sense-Integrate-Retrieve-Analyze-Feedback’ framework. Various digital technologies, such as the Internet of Things (IoT), Building Information Modeling (BIM), and machine learning, are utilized to integrate and visualize multi-source heterogeneous data through a lightweight web-based interface so that data retrieval, rapid prediction, and intelligent control are feasible based on various backend algorithm models. Compared to traditional measurement methods, this system offers broader spatiotemporal coverage, increased mobility, and less disruption to physical infrastructure, which underscores the sophistication of monitoring technology, data integration, and environmental management. The proposed intelligent platform has been validated in a traditional Chinese dwelling, and practical results demonstrate that most air quality problems occur in the early morning of winter while this system can swiftly predict and issue control signals for adjustments in occupied spaces exceeding predefined healthy thresholds. Field measurements confirmed that most air quality problems can be solved within 30 min after their appearance. By eliminating data silos through real-time synchronization and remote intelligent control via the digital platform, it bridges the gap between problem identification by managers and problem resolution by end-users, ensuring timely responses to indoor air quality concerns.

Study on Spatio-Temporal Patterns of Commuting under Adverse Weather Events: Case Study of Typhoon In-Fa

This study focuses on the main urban area of Yangzhou City and conducts a quantitative comparative analysis of traffic accessibility during normal weather and extreme precipitation conditions (typhoon) based on GPS trajectories of buses. From both temporal and spatial dimensions, it comprehensively examines the impact of extreme precipitation on bus travel speed, travel time, and the commuting range of residents in the main urban area of Yangzhou City. (1) Through the mining and analysis of multi-source heterogeneous big data (bus GPS trajectory data, bus network data, rainfall remote sensing data, and road network data), it is found that the rainstorm weather greatly affects the average speed and travel time of buses. In addition, when the intensity of heavy rainfall increases (decreases), the average bus speed and travel time exhibit varying degrees of spatio-temporal change. During the morning and evening rush hour commuting period of rainstorm weather, there are obvious differences in the accessibility change in each typical traffic community in the main urban area of Yangzhou city. In total, 90% of the overall accessibility change value is concentrated around −5 min~5 min, and the change range is concentrated around −25~10%. (2) To extract the four primary traffic districts (Lotus Pond, Slender West Lake, Jinghua City, and Wanda Plaza), we collected Points of Interest (POI) data from Amap and Baidu heat map, and a combination analysis of the employment–residence ratio model and proximity methods was employed. The result show that the rainstorm weather superimposed on the morning peak hour has different degrees of impact on the average speed of the above-mentioned traffic zones, with the most obvious impact on the Lotus Pond and the smallest impact on Wanda Plaza. Under the rainstorm weather, the traffic commute in the main urban area of Yangzhou in the morning and evening peak hour is basically normal. The results of this paper can help to quantify the impact of typhoon-rainstorm weather events on traffic commuting in order to provide a scientific basis for the traffic management department to effectively prevent traffic jams, ensure the reliability of the road network, and allow the traffic management department to more effectively manage urban traffic.

FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data

Centralised data management systems (e.g., data lakes) support queries over multi-source heterogeneous data. However, the query results from multiple sources commonly involve between-source conflicts, which makes query results unreliable and confusing and degrades the usability of centralised data management systems. Therefore, resolving the between-sourced conflicts is one of the most important problems for centralised data management systems. To solve it, many batch data fusion-based methods have been proposed, which require traversing all the data in the centralised data management systems and cause scalability and flexibility issues. To address these issues, this paper explores the problem of on-demand fusion queries, where the between-sourced conflicts are solved with only the query-related data; moreover, we propose an efficient on-demand fusion query framework, FusionQuery, which consists of a query stage and a fusion stage. In the query stage, we frame the heterogeneous data query problem as a knowledge graph matching problem and present a line graph-based method to accelerate it. In the fusion stage, we develop an Expectation Maximization-style algorithm to iteratively updates data veracity and source trustworthiness. Furthermore, we design an incremental estimation method of source trustworthiness to address the lack of sufficient observations. Extensive experiments on two real-world datasets demonstrate that FusionQuery outperforms state-of-the-art data fusion methods in terms of both effectiveness and efficiency.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things (IoT) is a significant trend in technology and a requirement for national strategic development. With the deepening digital transformation of the power grid, China’s power system has initially built a power IoT architecture comprising a perception, network, and platform application layer. However, owing to the structural complexity of the power system, the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment, diverse IoT protocol access methods, high concurrency of network communications, and weak data security protection. To address these issues, this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT, comprising cloud, pipe, edge, and terminal parts. It further reviews and analyzes the key technologies involved in the power IoT, such as the unified management of the physical model, high concurrent access, multi-protocol access, multi-source heterogeneous data storage management, and data security control, to provide a more flexible, efficient, secure, and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Research on Multi-Source Heterogeneous Big Data Fusion Method Based on Feature Level

With the development of research on multi-modal data fusion and its combination with online data management, the application of multi-modal big data fusion in information management systems is more and more extensive. How to integrate multi-modal big data effectively is the key technology to building an efficient information management system. In this paper, based on the combination of a multi-support vector machine and convolution neural network, the feature-level data fusion of multi-source heterogeneous big data is implemented, and it is applied to the real data set to test the relevant model. Experimental results show that this method can not only realize heterogeneous integration of big data, but also has high accuracy and reliability.

A Bayesian Deep Learning-Based Probabilistic Risk Assessment and Early-Warning Model for Power Systems Considering Meteorological Conditions

The ongoing process of decarbonizing the power system and the frequent occurrence of extreme weather have led to a significant increase in operating uncertainty and greatly reduced the system controllability. An effective risk assessment and early-warning tool will significantly assist system operators in monitoring and controlling power systems. However, existing methods mainly rely on simplified analytical conditional probability models to describe the component failure probability under different operating conditions and are not suitable for low-probability risk assessment. Inspired by the idea of Bayesian statistics, a Bayesian deep learning-based probabilistic risk assessment and early-warning (BDL-PRAEW) model considering meteorological conditions is proposed in this paper. A new Bayesian neural network (BNN) is proposed which efficiently utilizes expert experience and domain knowledge as prior to model the contingency probability. And a hybrid neural network is developed to rationally utilize the multi-source heterogeneous data and comprehensively analyze the historical and forecast information. Finally, a novel risk assessment and early-warning model for high-impact, low-probability (HILP) extreme events is proposed, which can predict the risk for the next n time-steps. The proposed method is tested on a modified IEEE 118-bus system and a real-world provincial grid, and the results verify its effectiveness.

A Job Recommendation Model Based on a Two-Layer Attention Mechanism

In the field of job recruitment, traditional recommendation methods only rely on users’ rating data of positions for information matching. This simple strategy has problems such as low utilization of multi-source heterogeneous data and difficulty in mining relevant information between recruiters and applicants. Therefore, this paper proposes a recurrent neural network model based on a two-layer attention mechanism. The model first improves the entity representation of recruiters and applicants through user behavior, company-related knowledge and other information. The entities and their combinations are then mapped to the vector space using one-hot and TransR methods, and a recurrent neural network with a two-layer attention mechanism is used to obtain their potential interests from the click sequence, and then a recommendation list is generated. The experimental results show that this model achieves better results than previous models.

Autonomous Data Association and Intelligent Information Discovery Based on Multimodal Fusion Technology

The effective association of multimodal data is the basis of massive multi-source heterogeneous data sharing in the era of big data. How to realize data autonomous association between massive multimodal databases and the automatic intelligent screening of valuable information from associated data, so as to provide a reliable data source for artificial intelligence (AI), is an urgent problem to be solved. In this paper, a data autonomous association method based on the organizational structure of data cells is proposed, including transaction abstraction based on information nucleuses, symmetric and asymmetric data association based on strategies and data pipes, and information generation based on big data. To screen meaningful data associations, an information-driven intelligent information discovery method and a task-driven intelligent information discovery method are proposed. The former screens meaningful data associations by training the reward and punishment model to simulate the manual scoring of data associations. The latter is task-oriented and screens meaningful data associations by training the reward and punishment model to simulate the manual ranking of data associations related to the task requests. Through the above work, autonomous data association and intelligent information discovery are effectively realized based on multimodal fusion technology, which provides a novel data source mining approach using multimodal data sharing and intelligent information discovery.

Assessing landslide susceptibility based on the random forest model and multi-source heterogeneous data

Landslides pose significant threats to human lives and property. The usefulness of mapping susceptibility in predicting these events, by providing early warning and implementing preventive measures, cannot be overstated. This study relies on the well-established grid management mechanism in Dengfeng and utilizes historical data collected from field surveys. Combining multi-source heterogeneous data obtained by aerospace technology, and using random forest model and feature selection, a landslide sensitivity assessment system based on space-ground collaboration was constructed. The study categorized areas according to their susceptibility to landslides using probabilistic forecasts. Specifically, regions were classified into four susceptibility categories: very low (landslide probabilities below 0.26), low (0.26 to 0.38), moderate (0.38 to 0.50), and high (above 0.50). This resulted in a comprehensive landslide susceptibility map that was rigorously analyzed and assessed. The findings indicate that: (1) high, moderate, low, and very low susceptibility areas for landslides in Dengfeng are 127.36, 527.49, 385.67, and 184.03 km2, respectively, accounting for 10.40 %, 43.08 %, 31.49 %, and 15.03 % of the total area. (2) The spatial distribution of landslides in Dengfeng exhibits a “multi-core clustering-radiate distribution” pattern, from the peripheral areas to the center. (3) The spatial distribution of high, moderate, low, and very low susceptibility areas for landslides is primarily influenced by factors such as distance from faults, elevation, and distance from rivers. The study has constructed a landslide sensitivity assessment system based on space-ground collaboration that can provide replicable research methods, provide scientific support for disaster reduction and prevention work, and reduce casualties and property losses caused by landslides.

Self-Supervised Multi-Source Heterogeneous Data Fusion Using Encode and Decode Attention for Intelligent Medical Device Communication Analysis

Self-Supervised Multi-Source Heterogeneous Data Fusion Using Encode and Decode Attention for Intelligent Medical Device Communication Analysis

Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion

Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion

Structured stochastic models based on multi-source heterogeneous data for predicting internal electricity load of non-residential buildings

Electricity load patterns in buildings are increasingly diverse and inherently present building-by-building and temporal variations. This study develops structured stochastic models to capture individual and temporal variations in non-residential building groups (e.g., education, office, culture, and retail buildings). Hourly electricity load data of non-residential buildings, categorized by building use, are sourced from two open datasets (Ministry of Land, Infrastructure and Transport and Korea Electric Power Corporation). First, heterogeneous public datasets are processed and utilized to provide both detailed building information and high-resolution electricity load data. Second, a structured set of four stochastic models are formulated using linear regression and multivariate normal distribution models. Model 1 utilizes the beta distribution to derive daily average electricity load per unit area for each building use. Model 2 utilizes the burr distribution to determine the daily variation of electricity load from the daily average. Models 3 and 4 utilize a multivariate normal distribution to represent the hourly average electricity load profile and its time variation, respectively. The coefficient of variation of the root mean square error (CVRMSE) for the structured probabilistic models ranges from 5.2 % to 14.8 % regarding five key load characterization variables. CVRMSEs for near-peak load, near-base load, rise time, high-load duration, and fall time are 8.5–13.9, 9.5–14.7, 7.3–14.8, 6.4–13.9, and 5.2–11.4 %, respectively. The validation results demonstrate that the proposed probabilistic models capture actual variations in measurements. Structured statistical models improve prediction performance by distinctly reflecting individual and temporal variations in electricity loads.

Quantification of micro-damage evolution process in ceramics through extensive analysis of multi-source heterogeneous data

Due to the randomness of hard and brittle material damage evolution, assessing damage and interpreting mechanisms is challenging. This study focuses on analyzing material damage mechanisms by employing extensive multi-source heterogeneous data to extract quantitative information. A machine vision-based method for detecting and assessing defects in ceramics was proposed, achieving 98 % accuracy. Leveraging this method in scratching experiments, the progression of damage and micro-failure mechanisms in Si3N4 was investigated through the analysis of defect area distribution, force and acoustical signals. Results revealed that the distribution of defect areas conforms to a three-parameter Weibull distribution. At scratch depths of 0.01, 0.02 mm, approximately 90 % of damage accumulated within 1e2–1e4 μm2; conversely, 0.03–0.06 mm led to a decrease to 30 – 40 %. Extensive crack propagation occurs within 0.02–0.03 mm, resulting in the intersecting formation of spalling blocks. Damage evolution occurs in three stages: shallow and small cracks with scattered spalling blocks in the first stage, deep and long cracks with continuous and irregularly shaped spalling blocks in the second stage, and nearly no cracks with continuous and regularly shaped blocks in the third stage. This study not only proposes an efficient method to detect and evaluate ceramic defects, but also deeply understands the evolution process of material damage mechanism. Considering defect distribution and statistical trends, this research not only provide guidance for the optimization of machining parameters, especially in the scene of specific precision and high removal rate. At the same time, the damage evolution and failure mechanism of Si3N4 provide useful experimental basis and data support for deepening the understanding of material behavior during ultra-precision grinding.