Using Element Clustering to Increase the Efficiency of XML Schema Matching

Schema matching attempts to discover semantic mappings between elements of two schemas. Elements are cross compared using various heuristics (e.g., name, data-type, and structure similarity). Seen from a broader perspective, the schema matching problem is a combinatorial problem with an exponential complexity. This makes the naive matching algorithms for large schemas prohibitively inefficient. In this paper we propose a clustering based technique for improving the efficiency of large scale schema matching. The technique inserts clustering as an intermediate step into existing schema matching algorithms. Clustering partitions schemas and reduces the overall matching load, and creates a possibility to trade between the efficiency and effectiveness. The technique can be used in addition to other optimization techniques. In the paper we describe the technique, validate the performance of one implementation of the technique, and open directions for future research

3D environment mapping using the Kinect V2 and path planning based on RRT algorithms

This paper describes a 3D path planning system that is able to provide a solution trajectory for the automatic control of a robot. The proposed system uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Our proposal includes a collision-free path planner based on the Rapidly-exploring Random Trees variant (RRT*), for a safe and optimal navigation of robots in 3D spaces. Results on RGB-D segmentation and recognition, point cloud processing, and comparisons between different RRT* algorithms, are presented.Peer ReviewedPostprint (published version

CARMA Large Area Star Formation Survey: Project Overview with Analysis of Dense Gas Structure and Kinematics in Barnard 1

We present details of the CARMA Large Area Star Formation Survey (CLASSy), while focusing on observations of Barnard 1. CLASSy is a CARMA Key Project that spectrally imaged N2H+, HCO+, and HCN (J=1-0 transitions) across over 800 square arcminutes of the Perseus and Serpens Molecular Clouds. The observations have angular resolution near 7" and spectral resolution near 0.16 km/s. We imaged ~150 square arcminutes of Barnard 1, focusing on the main core, and the B1 Ridge and clumps to its southwest. N2H+ shows the strongest emission, with morphology similar to cool dust in the region, while HCO+ and HCN trace several molecular outflows from a collection of protostars in the main core. We identify a range of kinematic complexity, with N2H+ velocity dispersions ranging from ~0.05-0.50 km/s across the field. Simultaneous continuum mapping at 3 mm reveals six compact object detections, three of which are new detections. A new non-binary dendrogram algorithm is used to analyze dense gas structures in the N2H+ position-position-velocity (PPV) cube. The projected sizes of dendrogram-identified structures range from about 0.01-0.34 pc. Size-linewidth relations using those structures show that non-thermal line-of-sight velocity dispersion varies weakly with projected size, while rms variation in the centroid velocity rises steeply with projected size. Comparing these relations, we propose that all dense gas structures in Barnard 1 have comparable depths into the sky, around 0.1-0.2 pc; this suggests that over-dense, parsec-scale regions within molecular clouds are better described as flattened structures rather than spherical collections of gas. Science-ready PPV cubes for Barnard 1 molecular emission are available for download.Comment: Accepted to The Astrophysical Journal (ApJ), 51 pages, 27 figures (some with reduced resolution in this preprint); Project website is at http://carma.astro.umd.edu/class