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Smooth topological surfaces embedded in 4D create complex internal structures in their projected 3D figures. Often these 3D figures twist, turn, and fold back on themselves, leaving important properties behind the surface sheets. Triangle meshes are not well suited for illustrating such internal structures and their topological features. In this paper, we propose a new approach to visualize these internal structures by slicing the 4D surfaces in our dimensions and revealing the underlying 4D structures using their cross-sectional diagrams. We think of a 4D-embedded surface as a collection of 3D curves stacked and evolved in time, very much like a 3D movie in a time-elapse form; and our new approach is to translate a surface in 4-space into such a movie — a sequence of time-lapse frames where successive terms in the sequence differ at most by a critical change. The visualization interface presented in this paper allows us to interactively define the longitudinal axis, and the automatic algorithms can partition the 4D surface into parallel slices and expose its internal structure by generating a time-lapse movie consisting of topologically meaningful cross-sectional diagrams from the representative slices. We have extracted movies from a range of known 4D mathematical surfaces with our approach. The results of the usability study show that the proposed slicing interface allows a mathematically true user experience with surfaces in four dimensions. 

R is the preferred language for Data analytics due to its open source development and high extensibility. Exponential growth in data has caused longer processing times leading to the rise in parallel computing technologies for analysis. Using R together with high performance computing resources is a cumbersome task. This paper proposes a framework that provides users with access to high-performance computing resources and simplifies the configuration, programming, uploading data and job scheduling through a web user interface. In addition to that, it provides two modes of parallelization of data-intensive computing tasks, catering to a wide range of users. The case studies emphasize the utility and efficiency of the framework. The framework provides better performance, ease of use and high scalability. 

With the increase in data-driven analytics, the demand for high performing computing resources has risen. There are many high-performance computing centers providing cyberinfrastructure (CI) for academic research. However, there exists access barriers in bringing these resources to a broad range of users. Users who are new to data analytics field are not yet equipped to take advantage of the tools offered by CI. In this paper, we propose a framework to lower the access barriers that exist in bringing the high-performance computing resources to users that do not have the training to utilize the capability of CI. The framework uses divide-and-conquer (DC) paradigm for data-intensive computing tasks. It consists of three major components - user interface (UI), parallel scripts generator (PSG) and underlying cyberinfrastructure (CI). The goal of the framework is to provide a user-friendly method for parallelizing data-intensive computing tasks with minimal user intervention. Some of the key design goals are usability, scalability and reproducibility. The users can focus on their problem and leave the parallelization details to the framework. 

As the volume of data and technical complexity of large-scale analysis increases, many domain experts desire powerful computational and familiar analysis interface to fully participate in the analysis workflow by just focusing on individual datasets, leaving the large-scale computation to the system. Towards this goal, we investigate and benchmark a family of Divide-and-Conquer strategies that can help domain experts perform large-scale simulations by scaling up their analysis code written in R, the most popular data science and interactive analysis language. We implement the Divide-and-Conquer strategies that use R as the analysis (and computing) language, allowing advanced users to provide custom R scripts and variables to be fully embedded into the large-scale analysis workflow in R. The whole process will divide large-scale simulations tasks and conquer tasks with Slurm array jobs and R. Simulations and final aggregations are scheduled as array jobs in parallel means to accelerate the knowledge discovery process. The objective is to provide a new analytics workflow for performing similar large-scale analysis loops where expert users only need to focus on the Divide-and-Conquer tasks with the domain knowledge.