Open Access

ARTICLE

Optimization of Electric Vehicle Charging Station Layout Based on Point of Interest Data and Location Entropy Evaluation

Journal on Big Data, Vol.6, pp. 21-41, 2024, DOI:10.32604/jbd.2024.057612 - 31 December 2024
Abstract This study introduces an electric vehicle charging station layout optimization method utilizing Point of Interest (POI) data, addressing traditional design limitations. It details the acquisition and visualization of POI data for Yancheng’s key locations and charging stations. Employing a hybrid K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, the study determines areas requiring optimization through location entropy and overlap analysis. The research shows that the integrated clustering approach can efficiently guide the fair distribution of charging stations, enhancing service quality and supporting the sustainable growth of the electric vehicle sector. More >

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An Overview of ETL Techniques, Tools, Processes and Evaluations in Data Warehousing

Journal on Big Data, Vol.6, pp. 1-20, 2024, DOI:10.32604/jbd.2023.046223 - 26 January 2024
Abstract The extraction, transformation, and loading (ETL) process is a crucial and intricate area of study that lies deep within the broad field of data warehousing. This specific, yet crucial, aspect of data management fills the knowledge gap between unprocessed data and useful insights. Starting with basic information unique to this complex field, this study thoroughly examines the many issues that practitioners encounter. These issues include the complexities of ETL procedures, the rigorous pursuit of data quality, and the increasing amounts and variety of data sources present in the modern data environment. The study examines ETL… More >

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794

Open Access

REVIEW

An Overview of ETL Techniques, Tools, Processes and Evaluations in Data Warehousing

Journal on Big Data, Vol.6, pp. 1-20, 2024, DOI:10.32604/jbd.2023.046223
Abstract The extraction, transformation, and loading (ETL) process is a crucial and intricate area of study that lies deep within the broad field of data warehousing. This specific, yet crucial, aspect of data management fills the knowledge gap between unprocessed data and useful insights. Starting with basic information unique to this complex field, this study thoroughly examines the many issues that practitioners encounter. These issues include the complexities of ETL procedures, the rigorous pursuit of data quality, and the increasing amounts and variety of data sources present in the modern data environment. The study examines ETL… More >

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794

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ARTICLE

Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

Journal on Big Data, Vol.5, pp. 1-18, 2023, DOI:10.32604/jbd.2023.041319
Abstract Movies are the better source of entertainment. Every year, a great percentage of movies are released. People comment on movies in the form of reviews after watching them. Since it is difficult to read all of the reviews for a movie, summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews. Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data. Opinion mining involves identifying and extracting the opinions of individuals, which can be positive, neutral, or negative. The… More >

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Open Access

ARTICLE

Analysis of Tourism Demand Difference Based on Data Mining and Intelligent Analysis

Journal on Big Data, Vol.5, pp. 69-84, 2023, DOI:10.32604/jbd.2023.046294
Abstract To serve as a reference for future foreign tourism study, relevant tourist sectors have done in-depth investigations on foreign tourism both domestically and internationally. A study of outbound tourism activities from the viewpoint of tourists can examine its development law and create successful marketing tactics based on the rise in the number of foreign tourists. Based on this, this study suggests a data mining technique to examine the variations in travel needs and marketing tactics among various consumer groups. The combined example analysis demonstrates how logical and useful our data mining analysis is. Our data More >

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466

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ARTICLE

Hybrid Model for Short-Term Passenger Flow Prediction in Rail Transit

Journal on Big Data, Vol.5, pp. 19-40, 2023, DOI:10.32604/jbd.2023.038249
Abstract A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation, assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation. First, the passenger flow sequence models in the study are broken down using VMD for noise reduction. The objective environment features are then added to the characteristic factors that affect the passenger flow. The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm. It is shown that the hybrid model VMD-CLSMT has a More >

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509

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ARTICLE

Author’s Age and Gender Prediction on Hotel Review Using Machine Learning Techniques

Journal on Big Data, Vol.5, pp. 41-56, 2023, DOI:10.32604/jbd.2022.044060
Abstract Author’s Profile (AP) may only be displayed as an article, similar to text collection of material, and must differentiate between gender, age, education, occupation, local language, and relative personality traits. In several information-related fields, including security, forensics, and marketing, and medicine, AP prediction is a significant issue. For instance, it is important to comprehend who wrote the harassing communication. In essence, from a marketing perspective, businesses will get to know one another through examining items and websites on the internet. Accordingly, they will direct their efforts towards a certain gender or age restriction based on… More >

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502

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ARTICLE

Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

Journal on Big Data, Vol.5, pp. 85-97, 2023, DOI:10.32604/jbd.2022.030908
Abstract At present, water pollution has become an important factor affecting and restricting national and regional economic development. Total phosphorus is one of the main sources of water pollution and eutrophication, so the prediction of total phosphorus in water quality has good research significance. This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform. By constructing the attribute object mapping relationship, the correlation between the two indicators was analyzed and used to predict the future data. Firstly, the monthly mean and daily mean concentrations of More >

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373

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Research on the Electric Energy Metering Data Sharing Method in Smart Grid Based on Blockchain

Journal on Big Data, Vol.5, pp. 57-67, 2023, DOI:10.32604/jbd.2023.044257
Abstract Enabling data sharing among smart grid power suppliers is a pressing challenge due to technical hurdles in verifying, storing, and synchronizing energy metering data. Access and sharing limitations are stringent for users, power companies, and researchers, demanding significant resources and time for permissions and verification. This paper proposes a blockchain-based architecture for secure and efficient sharing of electric energy metering data. Further, we propose a data sharing model based on evolutionary game theory. Based on the Lyapunov stability theory, the model’s evolutionary stable strategy (ESS) is analyzed. Numerical results verify the correctness and practicability of More >

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407

Open Access

ARTICLE

Optimization of Electric Vehicle Charging Station Layout Based on Point of Interest Data and Location Entropy Evaluation

Journal on Big Data, Vol.6, pp. 21-41, 2024, DOI:10.32604/jbd.2024.057612
Abstract This study introduces an electric vehicle charging station layout optimization method utilizing Point of Interest (POI) data, addressing traditional design limitations. It details the acquisition and visualization of POI data for Yancheng’s key locations and charging stations. Employing a hybrid K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, the study determines areas requiring optimization through location entropy and overlap analysis. The research shows that the integrated clustering approach can efficiently guide the fair distribution of charging stations, enhancing service quality and supporting the sustainable growth of the electric vehicle sector. More >

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224

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168