This paper addresses the challenge of multi-agent path planning for efficient data collection in dynamic, uncertain environments, exemplified by autonomous underwater vehicles (AUVs) navigating the Gulf of Mexico. Traditional greedy algorithms, though computationally efficient, often fall short in long-term planning due to their short-sighted nature, missing crucial data collection opportunities and increasing exposure to hazards. To address these limitations, we introduce WAITR (Weighted Aggregate Inter-Temporal Reward), a novel path-planning framework that integrates a knowledge graph with pathlet-based planning, segmenting the environment into dynamic, speed-adjusted sub-regions (pathlets). This structure enables coordinated, adaptive planning, as agents can operate within time-bound regions while dynamically responding to environmental changes. WAITR’s cumulative scoring mechanism balances immediate data collection with long-term optimization of Points of Interest (POIs), ensuring safer navigation and comprehensive data coverage. Experimental results show that WAITR substantially improves POI coverage and reduces exposure to hazards, achieving up to 27.1% greater event coverage than traditional greedy methods.

Spatiotemporal networks’ observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet), linking observational nodes (e.g., surveillance cameras) to events within defined geographical regions, enabling efficient monitoring. Using data from Real-Time Crime Camera (RTCC) systems and Calls for Service (CFS) in New Orleans, where RTCC combats rising crime amidst reduced police presence, we address the network’s initial observational imbalances. Aiming for uniform observational efficacy, we propose the Proximal Recurrence approach. It outperformed traditional clustering methods like k-means and DBSCAN by offering holistic event frequency and spatial consideration, enhancing observational coverage.

This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observable nodes, and edges that connect observer nodes to observable nodes. The coverage and coverage robustness scores evaluate the effectiveness of the observer node placements. This measure is beneficial for stochastic data as it may be coupled with Monte Carlo simulations to identify optimal placements for new observer nodes. In this paper, we construct a Geo-SpatioTemporal Bipartite Network (GSTBN) within the stochastic and dynamical environment of the Gulf of Mexico. This GSTBN consists of GCOOS sensor nodes and HYCOM Region of Interest (RoI) event nodes. The goal is to identify optimal placements to expand GCOOS to improve the forecasting outcomes by the HYCOM ocean prediction model.

Aggregate K Nearest Neighbor (AKNN) queries are problematic when performed within spatial networks. While simpler network queries may be solved by a single network traversal search, the AKNN requires a large number costly network distance computations to completely compute results. The M-Tree index, when used with Road Network Embedding, provides an efficient alternative which can return estimates of the AKNN results. The M-Tree index can then be used as a filter for AKNN results by quickly computing a superset of the query results. The final AKNN query results can be computed by sorting the results from the M-Tree. In comparison to Incremental Euclidean Restriction (IER), the M-Tree reduces the overall query processing time and the total number of necessary network distance computations required to complete a query. In addition, the M-Tree filtering method is tunable to allow increasing performance at the expense of accuracy, making it suitable for a wide variety of applications.

Spatial networks, such as road systems, operate differently from normal geospatial systems because objects are constrained to locations on the network. Performing queries on spatial networks demands entirely different solutions. Most spatial queries make use of an R-Tree to process them efficiently. The M-Tree is a data tree index which is capable of indexing data in any metric space. The M-Tree index can replace the R-Tree index for spatial network queries, such as range and KNN queries. The difficulty is that the M-Tree is only as efficient as the distance algorithm used on the underlying objects. Most network distance algorithms, such as A*, are too slow to allow the M-Tree to operate efficiently on spatial networks. The truncated road network embedding (tRNE) maps the network into a higher dimensional space where any LP metric can be used to efficiently compute an accurate approximation of network distance. The M-Tree combined with tRNE creates an efficient index structure for computing spatial network queries. The M-Tree substantially outperforms network expansion, the most popular method of computing spatial network queries, when performing spatial network KNN and range queries.

3-D terrain representation plays an important role in a number of terrain database applications. Hierarchical Triangulated Irregular Networks (TINs) provide a variable-resolution terrain representation that is based on a nested triangulation of the terrain. This paper compares and analyzes existing hierarchical triangulation techniques. The comparative analysis takes into account how aesthetically appealing and accurate the resulting terrain representation is. Parameters, such as adjacency, slivers, and streaks, are used to provide a measure on how aesthetically appealing the terrain representation is. Slivers occur when the triangulation produces thin and slivery triangles. Streaks appear when there are too many triangulations done at a given vertex. Simple mathematical expressions are derived for these parameters, thereby providing a fairer and a more easily duplicated comparison. In addition to meeting the adjacency requirement, an aesthetically pleasant hierarchical TINs generation algorithm is expected to reduce both slivers and streaks while maintaining accuracy. A comparative analysis of a number of existing approaches shows that a variant of a method originally proposed by Scarlatos exhibits better overall performance.

Recent studies have shown that the Vector Product Format (VPF) and most VPF-based products no longer meet the needs of the Modelling and Simulation (M&S) community of the Army, Navy, and Marine Corps. The research presented in this paper addresses some of the deficiencies outlined in these requirement surveys. One of the goals of this research is an Extended Vector Product Format (EVPF) and an EVPF-based product Modelling and Simulation Extended Vector Product (MSEVP). EVPF, which remains within the confines of VPFs static georelational data structure, extends VPF to allow for the efficient storage and access of Triangulated Irregular Network (TIN)-based three dimensional elevation data. Additionally, EVPF permits the efficient integration of terrain elevation data with ground surface features, and provides an elegant method of rendering terrain in three-dimensions. This paper documents the design of EVPF and highlights its salient features. It also reports on our progress toward the design and implementation of an MSEVP prototype. MSEVP is expected to better serve the needs of the M&S community by addressing some of the deficiencies outlined in recent requirement studies. The paper will address: (a)the generation of TIN based three-dimensional terrain data to populate the elevation coverage, (b) the extraction and conditioning of the transportation network to populate the associated coverage and (c) the integration of the transportation and elevation coverages.

Recent studies have shown that the Vector Product Format (VPF) and most VPF-based products no longer meet the needs of the Modeling and Simulation (M&S) community of the Army, Navy, and Marine Corps.The research presented in this paper addresses some of the deficiencies outlined in these requirement surveys.One of the goals of this research is an Extended Vector Product Fortnat (EVPF) and an EVPF-based product "Modeling and Simulation

It is shown that the existence of duplicate values in some attribute columns has a significant impact on the computational complexity of the sorting and joining operations. This is especially true when the number of distinct tuple values is a small fraction of the total number of tuples. The authors characterize a multirelation M(n, L) by its cardinality n and the number of distinct elements L it contains. Under this characterization, the worst time complexity of sorting such a multirelation with binary comparisons as basic operations is investigated. Upper and lower bounds on the number of three-branch comparisons needed to sort such a multirelation are established. Thereafter, the methodology used to study the complexity of sorting is applied to the natural join operation. It is shown that the existence of duplicate values in the join attribute columns can be exploited to reduce the computational complexity of the natural join operation.>