会議情報

SSTD 2025: International Symposium on Spatial and Temporal Databases

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提出日:
2025-04-27
通知日:
2025-07-01
会議日:
2025-08-25
場所:
Osaka, Japan
年:
19
CCF: c   CORE: a   QUALIS: b1   閲覧: 48441   追跡: 37   出席: 8

論文募集

SSTD 2025 (International Symposium on Spatial and Temporal Databases) is a CCF C / CORE A / QUALIS B1 conference held in Osaka, Japan on 2025-08-25. The paper submission deadline is 2025-04-27. Acceptance notifications are sent on 2025-07-01.

Scope The 19th International Symposium on Spatial and Temporal Data (SSTD) expands its scope beyond traditional databases to embrace the broader and rapidly evolving landscape of spatial and temporal data. While previous editions focused on foundational database systems and techniques, this year’s symposium welcomes research that addresses the full spectrum of challenges and innovations in managing, analyzing, and leveraging spatial and temporal data. We invite contributions spanning data management, analytics, mining, machine learning, visualization, and real-world applications across diverse domains, reflecting the growing significance of spatial and temporal data in today’s data-driven world. This expanded scope aims to foster interdisciplinary discussions and accommodate emerging trends where spatial and temporal data intersect with cutting-edge technologies. Topics of Interest Topics of interest include, but are not limited to, the following in the context of spatial, temporal, and spatio-temporal data. Spatial Foundations Spatial and temporal data models, representations, and standards Computational geometry algorithms Theoretical frameworks and complexity analysis Spatial approximation, sampling, and compression strategies Topological, geometric, and semantic relationships Spatial Data Mining, Machine Learning, and Analytics Foundation models and LLMs for spatial data Data warehousing and decision support Pattern recognition, clustering, and outlier detection Predictive modeling and forecasting Integration of deep learning and Geo-AI techniques Recommender systems Spatial Statistical Analysis Spatial autocorrelation and regression models Spatial point process analysis Geostatistical methods Bayesian models Analysis of spatial heterogeneity Spatio-temporal statistical methods Data Management and Storage Data types and query languages Database design and conceptual modeling Storage and retrieval techniques Access methods and indexing Query processing and optimization Distributed and cloud-based architectures Stream processing and real-time indexing Moving objects applications Methods for data integration and harmonization Spatial Networks and Graphs Transportation and mobility analytics Network modeling and route optimization Graph-based techniques Sensor networks Geographic Information Retrieval (GIR) Spatially-aware search engines Geotagging and geographic text analysis Location-based information retrieval Integration of spatial and textual data Ontologies and taxonomies Semantic analysis of geographic entities Spatial-keyword search Visualization and Interaction Novel visualization techniques Human-computer interaction AR/VR environments and immersive visualizations Quality, Uncertainty, Fairness and Privacy Data quality assessment and cleaning Handling uncertainty and incomplete data Privacy-preserving methods Data bias, fairness, transparency Applications and Emerging Domains Location-based services and social networks Experiments and performance evaluation Smart cities and urban planning Public health and emergency management IoT-based sensor networks Computer vision for spatial data Crowdsourcing and user-generated content Indoor spaces and mobility Spatial quantum modeling New hardware platforms
最終更新 Dou Sun

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関連仕訳帳

CCF完全な名前インパクト ・ ファクター出版社ISSN
Spatial Statistics2.5Elsevier2211-6753
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