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 1225
Special Issue Editors
Interests: data stream mining; graph data mining; algorithm fairness
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
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
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Keywords
- knowledge graph
- mobility data
- trajectory
- algorithm design
- data stream
- distributed system
- federated learning
- privacy
- deep learning
- big data
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