In this paper we explore the use of spatial clustering algorithms as a new computational approach for modeling the cosmic web. We demonstrate that such algorithms are efficient in terms of computing time needed. We explore three distinct spatial methods which we suitably adjust for (i) detecting the topology of the cosmic web and (ii) categorizing various cosmic structures as voids, walls, clusters and superclusters based on a variety of topological and physical criteria such as the physical distance between objects, their masses and local densities. The methods explored are (1) a new spatial method called Gravity Latticexs; (2) a modified version of another spatial clustering algorithm, the ABACUS; and (3) the well known spatial clustering algorithm HDBSCAN. We utilize HDBSCAN in order to detect cosmic structures and categorize them using their overdensity. We demonstrate that the ABACUS method can be combined with the classic DTFE method to obtain similar results in terms of the achieved accuracy with about an order of magnitude less computation time. To further solidify our claims, we draw insights from the computer science domain and compare the quality of the results with and without the application of our method. Finally, we further extend our experiments and verify their effectiveness by showing their ability to scale well with different cosmic web structures that formed at different redshifts.

Contemporary applications require the processing of large, high-velocity streams of symbolic events derived from sensor data. A complex event recognition (CER) system processes these symbolic events online and reports the satisfaction of complex event patterns with minimal latency. We extend an Event Calculus dialect optimised for online CER with Allen’s interval algebra, in order to provide more accurate event patterns. We demonstrate the effectiveness of our system on real data streams from maritime situational awareness.