Applied Computing
Summary
Applied Computing harnesses computational methods and technologies to address practical challenges across science, engineering and society. It encompasses the development and deployment of software and hardware solutions—from data processing and simulation to intelligent systems and embedded devices—tailored for specific domains such as healthcare, environmental management, logistics and digital media. Practitioners integrate algorithm design, systems engineering and user-centred practices to ensure robust, scalable and secure applications. Key concerns include optimising performance on modern architectures, managing large and heterogeneous data sources, and embedding tools—such as machine learning, spatial analytics or real-time control—into operational workflows. By bridging theoretical advances and real-world requirements, Applied Computing drives innovation in industry and research, underpins digital infrastructure and contributes to societal resilience and competitiveness on a global scale.
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Research from all publishers
Recent work on spatial information retrieval has introduced a formal taxonomy of spatial queries based on underlying relationships—topological, metric and directional. By rigorously classifying these query types, this research clarifies implementation techniques for spatial predicates and identifies open challenges in expressive power and indexing for large-scale geospatial datasets.
A hybrid approach to qualitative spatial reasoning has been developed by embedding established symbolic calculi into a probabilistic framework. This method combines crisp topological and directional rules with Markov logic networks to allow uncertain evidence to propagate through spatial inferences, improving robustness in tasks such as direction estimation under noisy observations.
Foundational advances in topological modelling have extended the classic nine-intersection matrix to a 27-intersection formalism. By considering intersections of interiors, boundaries and exteriors across points, lines and regions, this model distinguishes a richer set of spatial relations while maintaining interoperability with existing standards, thereby enhancing precision in geographic information systems.
Applied Computing publication trend
The graph below shows the total number of articles in applied computing across all publications each year (not limited to Nature Index journals).
Technical terms
Spatial query: A database operation that selects or retrieves geographic features based on specified spatial relations or geometric criteria.
Topological relation: An abstract spatial relationship—such as adjacency, containment or overlap—preserved under continuous deformation.
Qualitative spatial calculus: A symbolic framework for representing and reasoning about relative spatial configurations without precise numerical measurements.
Markov logic network: A probabilistic model that integrates first-order logic with weighted formulas to support uncertain inference.
Nine-intersection model (9IM): A matrix formalism classifying topological relations by examining intersections among interiors, boundaries and exteriors of two spatial objects.
27-intersection model: An extended intersection matrix that refines the 9IM by accounting for additional intersection cases among points, lines and regions in two dimensions.
References
- Defining and designing spatial queries: the role of spatial relationships. Geo-spatial Information Science (2023).
- Qualitative spatial reasoning with uncertain evidence using Markov logic networks. International Journal of Geographical Information Science (2023).
- A 27-Intersection Model for Representing Detailed Topological Relations between Spatial Objects in Two-Dimensional Space. ISPRS International Journal of Geo-Information (2017).
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