Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.1
2.8
Journals
Future Internet
Special Issues
Distributed Storage of Large Knowledge Graphs with Mobility Data
share announcement

Distributed Storage of Large Knowledge Graphs with Mobility Data

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 4453

Special Issue Editors

Dr. Yanhao Wang

E-Mail Website
Guest Editor
School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
Interests: data stream mining; graph data mining; algorithm fairness
Prof. Dr. Michael Sheng

E-Mail Website
Guest Editor
School of Computing, Faculty of Science & Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: web of things; internet of things; big data analytics; web science; service-oriented computing; pervasive computing; sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Chengcheng Yang

E-Mail Website
Guest Editor
School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
Interests: database; storage system

Special Issue Information

Dear Colleagues,

In today’s big data and AI era, a huge amount of mobility data is generated from heterogeneous sources, such as social media, GPS, and the Internet of Things (IoT). Managing and analyzing such big mobility data, often organized in the form of knowledge graphs (KGs), is essential in many real-world applications including location-based services, recommender systems, smart transportation, and digital economy. However, there are still several great challenges in mobility data management and mining. For example, mobility data are often received incrementally over time and should be stored in a distributed manner. As another example, mobility data often contain sensitive user information and should be analyzed with privacy concerns. To bridge these gaps, researchers and engineers are developing new and enhancing existing techniques and methods to improve the performance of mobility data analytics.

The goal of this Special Issue is to provide an overview of the latest developments regarding mobility data, knowledge graphs, and distributed systems. Both theoretical and technical aspects are of interest. Interdisciplinary approaches are also highly welcome.

Topics of interest include but are not limited to the following:
•    Distributed and/or stream processing of mobility data and knowledge graphs;
•    Data structures and algorithms for mobility data and knowledge graphs;
•    Machine learning and deep learning on mobility data and knowledge graphs;
•    Federated learning on mobility data and knowledge graphs;
•    Security and privacy issues on mobility data and knowledge graph analytics;
•    Fairness, transparency, and interpretability of mobility data and knowledge graph analytics;
•    Other novel applications and emerging technologies for mobility data and knowledge graphs.

Dr. Yanhao Wang
Prof. Dr. Michael Sheng
Dr. Chengcheng Yang
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • knowledge graph
  • mobility data
  • trajectory
  • algorithm design
  • data stream
  • distributed system
  • federated learning
  • privacy
  • deep learning
  • big data

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

31 pages, 5936 KiB  
Article
Advanced Optimization Techniques for Federated Learning on Non-IID Data
by Filippos Efthymiadis, Aristeidis Karras, Christos Karras and Spyros Sioutas
Future Internet 2024, 16(10), 370; https://doi.org/10.3390/fi16100370 - 13 Oct 2024
Viewed by 904
Abstract
Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy. In this paper, we investigate the impact of Non-Independent and Identically Distributed (non-IID) data on the performance of federated [...] Read more.
Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy. In this paper, we investigate the impact of Non-Independent and Identically Distributed (non-IID) data on the performance of federated training, where we find a reduction in accuracy of up to 29% for neural networks trained in environments with skewed non-IID data. Two optimization strategies are presented to address this issue. The first strategy focuses on applying a cyclical learning rate to determine the learning rate during federated training, while the second strategy develops a sharing and pre-training method on augmented data in order to improve the efficiency of the algorithm in the case of non-IID data. By combining these two methods, experiments show that the accuracy on the CIFAR-10 dataset increased by about 36% while achieving faster convergence by reducing the number of required communication rounds by 5.33 times. The proposed techniques lead to improved accuracy and faster model convergence, thus representing a significant advance in the field of federated learning and facilitating its application to real-world scenarios. Full article
(This article belongs to the Special Issue Distributed Storage of Large Knowledge Graphs with Mobility Data)
►▼ Show Figures

Figure 1

13 pages, 9262 KiB  
Article
Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
by Jesus Calle-Cancho, Carlos Cañada, Rafael Pastor-Vargas, Mercedes E. Paoletti and Juan M. Haut
Future Internet 2024, 16(9), 342; https://doi.org/10.3390/fi16090342 - 20 Sep 2024
Viewed by 560
Abstract
With the rapid advancement of the Internet of Things and the emergence of 6G networks in smart city environments, a growth in the generation of data, commonly known as big data, is expected to consequently lead to higher latency. To mitigate this latency, [...] Read more.
With the rapid advancement of the Internet of Things and the emergence of 6G networks in smart city environments, a growth in the generation of data, commonly known as big data, is expected to consequently lead to higher latency. To mitigate this latency, mobile edge computing has been proposed to alleviate a portion of the workload from mobile devices by offloading it to nearby edge servers equipped with appropriate computational resources. However, existing solutions often exhibit poor performance when confronted with complex network topologies. Thus, this paper introduces a decentralized mechanism aimed at determining the locations of network edge nodes in such complex network topologies, characterized by lengthy execution times. Our proposal provides performance improvements and offers scalability and flexibility as networks become more complex. Experimental evaluations are conducted using the Shanghai Telecom dataset to validate our proposed approach. Full article
(This article belongs to the Special Issue Distributed Storage of Large Knowledge Graphs with Mobility Data)
►▼ Show Figures

Figure 1

16 pages, 698 KiB  
Article
Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction
by Chaelim Park, Hayoung Lee and Ok-ran Jeong
Future Internet 2024, 16(8), 260; https://doi.org/10.3390/fi16080260 - 24 Jul 2024
Viewed by 1540
Abstract
The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of [...] Read more.
The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health, combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management. Full article
(This article belongs to the Special Issue Distributed Storage of Large Knowledge Graphs with Mobility Data)
►▼ Show Figures

Figure 1

Review

Jump to: Research

22 pages, 2551 KiB  
Review
A Performance Benchmark for the PostgreSQL and MySQL Databases
by Sanket Vilas Salunke and Abdelkader Ouda
Future Internet 2024, 16(10), 382; https://doi.org/10.3390/fi16100382 - 19 Oct 2024
Viewed by 735
Abstract
This study highlights the necessity for efficient database management in continuous authentication systems, which rely on large-scale behavioral biometric data such as keystroke patterns. A benchmarking framework was developed to evaluate the PostgreSQL and MySQL databases, minimizing repetitive coding through configurable functions and [...] Read more.
This study highlights the necessity for efficient database management in continuous authentication systems, which rely on large-scale behavioral biometric data such as keystroke patterns. A benchmarking framework was developed to evaluate the PostgreSQL and MySQL databases, minimizing repetitive coding through configurable functions and variables. The methodology involved experiments assessing select and insert queries under primary and complex conditions, simulating real-world scenarios. Our quantified results show PostgreSQL’s superior performance in select operations. In primary tests, PostgreSQL’s execution time for 1 million records ranged from 0.6 ms to 0.8 ms, while MySQL’s ranged from 9 ms to 12 ms, indicating that PostgreSQL is about 13 times faster. For select queries with a where clause, PostgreSQL required 0.09 ms to 0.13 ms compared to MySQL’s 0.9 ms to 1 ms, making it roughly 9 times more efficient. Insert operations were similar, with PostgreSQL at 0.0007 ms to 0.0014 ms and MySQL at 0.0010 ms to 0.0030 ms. In complex experiments with simultaneous operations, PostgreSQL maintained stable performance (0.7 ms to 0.9 ms for select queries during inserts), while MySQL’s performance degraded significantly (7 ms to 13 ms). These findings underscore PostgreSQL’s suitability for environments requiring low data latency and robust concurrent processing capabilities, making it ideal for continuous authentication systems. Full article
(This article belongs to the Special Issue Distributed Storage of Large Knowledge Graphs with Mobility Data)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop