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Big Data Analytics and Information Technology for Smart Cities and...
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Big Data Analytics and Information Technology for Smart Cities and Citizen Wellbeing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 5 January 2025 | Viewed by 1529

Special Issue Editors

Dr. Jiashu Zhao

E-Mail Website
Guest Editor
Department of Physic and Computer Science, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
Interests: information retrieval; machine learning; big data; recommendation
Prof. Dr. Jingyuan Li

E-Mail Website
Guest Editor
School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 102401, China
Interests: social network data analysis and network evolutionary computing; personalized knowledge generation and communication
Dr. Li Zhang

E-Mail Website
Guest Editor
Institute of Finance and Technology, University College London, London WC1E 6BT, UK
Interests: machine learning; graph representation learning; graph neural network; social network analysis; financial network analysis

Special Issue Information

Dear Colleagues,

Cities are becoming data troves as information technology advances. Sensors, cameras and connected devices continuously collect a vast array of urban data. This Special Issue aims to explore how to harness the power of big data analytics techniques to promote actionable insights for smarter, more sustainable urban environments. By leveraging cutting-edge analytical tools, such as statistical analysis, deep learning, reinforcement learning and large language models, researchers and urban planners can glean actionable insights from this rich data pool. The potential benefits of a data-driven approach to urban planning are significant:

  • Traffic and Infrastructure: Intelligent traffic management systems informed by Big Data analysis can significantly reduce congestion. Predictive maintenance of city infrastructure can minimize disruptions and optimize resource allocation.
  • Sustainability: Data-driven insights can facilitate the design of green urban spaces, optimize waste management systems and improve water management strategies.
  • Public Safety: Real-time crime analytics empowered by Big Data can enhance public safety efforts and lead to improved emergency response coordination.
  • Smart Home: Smart cities are formed together with countless smart families, where the widespread and successful applications of smart voice interaction, VR/AR glasses and large language models can significantly better the quality of living for individuals in the city.
  • Smart Education: Current education is no longer limited to the campus, but rather a combination of educational resources from the campus, family and society, which requires the support of AIGC technology for education content generation, etc.

However, unlocking the full potential of big data for smart cities necessitates addressing several crucial challenges, particularly data security and privacy. Effective and robust frameworks are needed to ensure responsible collection, storage and utilization of data. This Special Issue brings together leading researchers and practitioners to explore these challenges and opportunities. By delving into innovative big data-driven approaches and showcasing real-world applications, we aim to contribute to the ongoing dialogue on building smarter, more efficient and livable cities for the future.

Dr. Jiashu Zhao
Prof. Dr. Jingyuan Li
Dr. Li Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data analysis
  • machine learning
  • deep learning
  • large language model
  • social network security
  • smart city
  • smart home
  • smart traffic
  • smart education

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Published Papers (2 papers)

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Research

23 pages, 696 KiB  
Article
KG-EGV: A Framework for Question Answering with Integrated Knowledge Graphs and Large Language Models
by Kun Hou, Jingyuan Li, Yingying Liu, Shiqi Sun, Haoliang Zhang and Haiyang Jiang
Electronics 2024, 13(23), 4835; https://doi.org/10.3390/electronics13234835 - 7 Dec 2024
Viewed by 566
Abstract
Despite the remarkable progress of large language models (LLMs) in understanding and generating unstructured text, their application in structured data domains and their multi-role capabilities remain underexplored. In particular, utilizing LLMs to perform complex reasoning tasks on knowledge graphs (KGs) is still an [...] Read more.
Despite the remarkable progress of large language models (LLMs) in understanding and generating unstructured text, their application in structured data domains and their multi-role capabilities remain underexplored. In particular, utilizing LLMs to perform complex reasoning tasks on knowledge graphs (KGs) is still an emerging area with limited research. To address this gap, we propose KG-EGV, a versatile framework leveraging LLMs to perform KG-based tasks. KG-EGV consists of four core steps: sentence segmentation, graph retrieval, EGV, and backward updating, each designed to segment sentences, retrieve relevant KG components, and derive logical conclusions. EGV, a novel integrated framework for LLM inference, enables comprehensive reasoning beyond retrieval by synthesizing diverse evidence, which is often unattainable via retrieval alone due to noise or hallucinations. The framework incorporates six key stages: generation expansion, expansion evaluation, document re-ranking, re-ranking evaluation, answer generation, and answer verification. Within this framework, LLMs take on various roles, such as generator, re-ranker, evaluator, and verifier, collaboratively enhancing answer precision and logical coherence. By combining the strengths of retrieval-based and generation-based evidence, KG-EGV achieves greater flexibility and accuracy in evidence gathering and answer formulation. Extensive experiments on widely used benchmarks, including FactKG, MetaQA, NQ, WebQ, and TriviaQA, demonstrate that KG-EGV achieves state-of-the-art performance in answer accuracy and evidence quality, showcasing its potential to advance QA research and applications. Full article
(This article belongs to the Special Issue Big Data Analytics and Information Technology for Smart Cities and Citizen Wellbeing)
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12 pages, 2388 KiB  
Article
Analyzing the Relationship Between COVID-19 and Sociodemographic and Environmental Factors: A Case Study in Toronto
by Brian Anlan Yu and Qinmin Vivian Hu
Electronics 2024, 13(22), 4524; https://doi.org/10.3390/electronics13224524 - 18 Nov 2024
Viewed by 474
Abstract
COVID-19 has disproportionately impacted communities based on sociodemographic and environmental factors. Previous studies have largely focused on traditional statistical models to investigate these disparities with limited attention to within-city variations. This research addresses this gap by employing advanced machine learning models to predict [...] Read more.
COVID-19 has disproportionately impacted communities based on sociodemographic and environmental factors. Previous studies have largely focused on traditional statistical models to investigate these disparities with limited attention to within-city variations. This research addresses this gap by employing advanced machine learning models to predict COVID-19 case counts at the neighborhood level within Toronto. Using algorithms such as Support Vector Regression, Random Forest, Gradient Boosting, and XGBoost, along with SHAP (SHapley Additive exPlanations) analysis, we identify key factors impacting COVID-19 transmission, including air pollution, socioeconomic status, and racialized group membership. Our results demonstrate that sociodemographic factors significantly influence sporadic cases, while environmental factors, particularly air pollutants, are critical in outbreak cases. This study highlights the value of machine learning in understanding complex interactions between risk factors with implications for targeted public health interventions to mitigate COVID-19 disparities. Full article
(This article belongs to the Special Issue Big Data Analytics and Information Technology for Smart Cities and Citizen Wellbeing)
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