R V College of Engineering
Density-based spatial clustering of data with noise (DBSCAN) is a popular clustering algorithm that groups data points which are close together using two parameters eps - which is the radius of each cluster, and Minpts, which is the minimum number of points in each cluster. However, the performance of DBSCAN reduces for the datasets with varying density clusters. The poster proposes the implementation of a novel distributed and adaptive DBSCAN algorithm on the HPCC Systems platform. The proposed approach uses techniques such as grid search and Gaussian kernel to search optimized values for the threshold density of clusters, thus eliminating the requirement for users to specify the parameters. Further, the experimental investigation suggests that proposed ADBSAN performs better compared to existing ADBSCAN implementations using k-dist and Gaussian kernels.
Masters in Computer Science
Deployment of HPCC Systems to commercial clouds can be done in multiple ways depending on various business needs. While lift-and-shift is one way to go which involves moving of unchanged application infrastructure from on-prem to the cloud based on virtual machine approach, containerization of the application with the aim to go cloud-native is another approach. The recent push of HPCC Systems to go cloud-native involves containerization strategy which provides a logical packaging mechanism in which HPCC resources are abstracted from the environment in which they run, with multiple containers running on top of the OS kernel directly.