Information Fusion for Multi-Source Material Data: Progress and Challenges

Zhou, Jingren; Hong, Xin; Jin, Peiquan

doi:10.3390/app9173473

Cited by 27 publications

(10 citation statements)

References 75 publications

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“…erefore, based on big data technology, the data are fused, and the fusion model is designed. At present, the methods of multisource information fusion mainly include unified identification, data comparison, heterogeneous weighting, and analysis modeling [11]. Big data about enterprise management can be generally divided into three categories, as shown in Figure 1.…”

Section: Key Technologies Of Big Data Management In Powermentioning

confidence: 99%

“…e specific process of data-level fusion is shown in Figure 6. e original data of data-level fusion of the power system are obtained by the advanced and reliable sensor technology in the power grid and the aggregation with the goal of information fusion [11]. With the rapid development of monitoring technology, it is easier to master the operation rules of equipment, and the fusion of the level becomes more accurate and comprehensive.…”

Section: Data-level Fusionmentioning

confidence: 99%

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Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

Qin

Liao

Chen

et al. 2021

Security and Communication Networks

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With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.

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Section: Key Technologies Of Big Data Management In Powermentioning

confidence: 99%

Section: Data-level Fusionmentioning

confidence: 99%

Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

Qin

Liao

Chen

et al. 2021

Security and Communication Networks

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“…e fusion of data is a necessity for the assurance of consistency and analysis of integrated data. Data fusion as a whole however is a black-box operation for the consumer that lacks interpretability and debugs capabilities for the data fusion process [17][18][19].…”

Section: Big Data Fusionmentioning

confidence: 99%

“…Zhou et al's study [17] contains a substantial amount of material data, mainly from multiple sources, with varying data formats and semantic links.…”

Section: Literature Reviewmentioning

confidence: 99%

BRScS Approach for Resolving Heterogeneity of Data from Multiple Resources at Semantic Level

Ramzan

Mushtaq

Ali

et al. 2022

Mathematical Problems in Engineering

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Data have multiplied at an exponential rate in the age of the Internet. Large amounts of data can be combined at this science hotspot. Making sense of big data has become increasingly difficult due to its volume, velocity, precision, and variety (sometimes referred to as heterogeneity). Many data sources are employed to create data heterogeneity. Big data fusion has both advantages and disadvantages when it comes to integrating data from a variety of sources. The focus of this work is on large data fusion using deep learning approaches to combine datasets from a variety of different sources. It is also possible to combine data from many sources. People are increasingly turning to the Internet and web-based services to meet their daily demands. Storage media can hold data in a variety of formats. Managing the vast volume of data is quite tough for an organization (referred to as “big data”). These data are rationally combined and incorporated into the system. Data fusion will be the subject of this paper. The process of collecting data and making judgments based on that data has become much more challenging as a result of technological advancements. The heterogeneity of data is made possible by the great volume, precision, and, most critically, variety of big data. A wide range of data sources can both help and hinder big-data converging. This study was created to introduce several methods and techniques for semantically merging huge datasets.

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“…Multi-source information fusion is an information processing technology that automatically processes the data from multiple information sources in some effective ways and transforms data into a support for human decision-making. It is generated from different channels and presented in various forms (text, image, audio, and numerical value), and it is a process of data fusion describing the same subject (Zhou et al, 2019;Ma & Bennett, 2021). There are multiple multi-source information fusion methods, which can be classified according to three criteria: information fusion processing hierarchy, structural model and fusion information type.…”

Section: Key Technology Analysismentioning

confidence: 99%

Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

2021

Journal of Organizational and End User Computing

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The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.

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Information Fusion for Multi-Source Material Data: Progress and Challenges

Cited by 27 publications

References 75 publications

Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

BRScS Approach for Resolving Heterogeneity of Data from Multiple Resources at Semantic Level

Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

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