A Study on a Spatiotemporal Entity-Based Event Data Model
Bibliographic Data
| ID | 22031319 |
|---|---|
| Authors | Mingming Wang (0000-0001-6053-7449, PLA Information Engineering University), Jiangshui Zhang (PLA Information Engineering University, corresponding author), Yibing Cao (0000-0002-4054-0656, PLA Information Engineering University), Shenghui Li (0000-0002-2687-2697, Henan College of Surveying and Mapping), Minjie Chen (0000-0002-0794-8561, PLA Information Engineering University) |
| Year | 2024 |
| Volume | 13 |
| Issue | 10 |
| Pages | 360 |
| Publication date | 2024-10-14 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi13100360 |
| OpenAlex | W4403381126 |
| Language | EN |
| Citations received | 1 |
| References cited | 23 |
An event is an important medium for recording, expressing, and understanding the real world. Additionally, a data model can provide a digital and structured description method for the real world. Therefore, studying event data models is highly important for describing the real world. By analyzing the representational categories of the existing event data models, the representation of existing event models was found to have different emphases and not be sufficiently balanced, and the universality and comprehensiveness need to be improved. Therefore, based on the advantages of the ontological event model in expressing semantic information and the advantages of the object-event-based spatiotemporal data model in expressing entity multidimensional characteristics and dynamic processes, a spatiotemporal entity-based event data model and the modeling method were designed to provide model support for event organization and processing. Additionally, the Long March and its important battles were selected as case studies to validate the proposed model. The validation shows that the proposed model performs well in terms of event dynamics, hierarchical structure, and complex interrelationships
Analytics · Data mining · Event data · Computer Science · Data Management and Algorithms · Data Quality and Management · Geographic Information Systems Studies
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| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |