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We propose and create a new data model for learning specific environments and learning analytics applications. This is motivated from the experience in the Fiber Bundle Data Model used for large - time and space dependent - data. Our proposed data model integrates file or stream-based data structures from capturing devices more easily. Learning analytics algorithms are added directly to the data, and formulation of queries and analytics is done in Python. It is designed to improve collaboration in the field of learning analytics. We leverage a hierarchical data structure, where varying data is located near the leaves. Abstract data types are identified in four distinct pathways, which allow storing most diverse data sources. We compare different implementations regarding its memory footprint and performance. Our tests indicate that LeAn Bundles can be smaller than a naïve xAPI export. The benchmarks show that the performance is comparable to a MongoDB, while having the benefit of being portable and extensible.
Point-based geometry representations have become widely used in numerous contexts, ranging from particle-based simulations, over stereo image matching, to depth sensing via light detection and ranging. Our application focus is on the reconstruction of curved line structures in noisy 3D point cloud data. Respective algorithms operating on such point clouds often rely on the notion of a local neighborhood. Regarding the latter, our approach employs multi-scale neighborhoods, for which weighted covariance measures of local points are determined. Curved line structures are reconstructed via vector field tracing, using a bidirectional piecewise streamline integration. We also introduce an automatic selection of optimal starting points via multi-scale geometric measures. The pipeline development and choice of parameters was driven by an extensive, automated initial analysis process on over a million prototype test cases. The behavior of our approach is controlled by several parameters — the majority being set automatically, leaving only three to be controlled by a user. In an extensive, automated final evaluation, we cover over one hundred thousand parameter sets, including 3D test geometries with varying curvature, sharp corners, intersections, data holes, and systematically applied varying types of noise. Further, we analyzed different choices for the point of reference in the co-variance computation; using a weighted mean performed best in most cases. In addition, we compared our method to current, publicly available line reconstruction frameworks. Up to thirty times faster execution times were achieved in some cases, at comparable error measures. Finally, we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets, extracting power line cables.