Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.Comment: 10 page

Bibliometric Perspectives on Medical Innovation using the Medical Subject Headings (MeSH) of PubMed

Multiple perspectives on the nonlinear processes of medical innovations can be distinguished and combined using the Medical Subject Headings (MeSH) of the Medline database. Focusing on three main branches-"diseases," "drugs and chemicals," and "techniques and equipment"-we use base maps and overlay techniques to investigate the translations and interactions and thus to gain a bibliometric perspective on the dynamics of medical innovations. To this end, we first analyze the Medline database, the MeSH index tree, and the various options for a static mapping from different perspectives and at different levels of aggregation. Following a specific innovation (RNA interference) over time, the notion of a trajectory which leaves a signature in the database is elaborated. Can the detailed index terms describing the dynamics of research be used to predict the diffusion dynamics of research results? Possibilities are specified for further integration between the Medline database, on the one hand, and the Science Citation Index and Scopus (containing citation information), on the other.Comment: forthcoming in the Journal of the American Society for Information Science and Technolog

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

From SpaceStat to CyberGIS: Twenty Years of Spatial Data Analysis Software

This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.

ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations.

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results