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Wildfires are destructive fires over the wildland that can wipe out large areas of vegetation and infrastructure. Such fires are hard to control and manage as they can change directions almost instantly, driven by changing environmental conditions. Effective response to such events requires the ability to monitor and predict the behavior of the fire as fast as they change. The WIFIRE project builds an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction, and visualization of wildfire behavior. One goal of WIFIRE is to provide the tools to predict a more accurate rate of a spreading wildfire. To this end, WIFIRE has developed interfaces for ingesting and visualizing high-density sensor networks to improve fire and weather predictions, and has created a data model for wildfire resources including sensed and archived data, sensors, satellites, cameras, modeling tools, workflows, and social information including Twitter feeds for wildfire research and response. This paper presents WIFIRE’s Firemap web platform to make these geospatial data and products accessible. Through a web browser, Firemap enables geospatial information visualization and a unified access to geospatial workflows using Kepler. Using GIS capabilities combined with scalable big data integration and processing, Firemap enables simple execution of the model with options for running ensembles by taking the information uncertainty into account. The results are easily viewable, sharable, repeatable, and can be animated as a time series. [ABSTRACT FROM AUTHOR]

This paper shows the extension of the wildfire simulation tool FARSITE to allow for data assimilation capabilities on both fire perimeters and fuel adjustment factors to improve the accuracy of wildfire spread predictions. While fire perimeters characterize the overall burn scar of a wildfire, fuel adjustment factors are fuel model specific calibration numbers that adjust the rate of spread for each fuel type independently. Data assimilation updates of both fire perimeters and fuel adjustment factors are calculated from an Ensemble Kalman Filter (EnKF) that exploits the uncertainty information on the simulated fire perimeter, fuel adjustment factors and a measured fire perimeter. The effectiveness of the proposed data assimilation is illustrated on a wildfire simulation representing the 2014 Cocos fire, tracking time varying fuel adjustment factors based on noisy and limited spatial resolution observations of the fire perimeter. [ABSTRACT FROM AUTHOR]

Modern web technologies facilitate the creation of high-quality data visualizations, and rich, interactive components across a wide variety of devices. Scientific workflow systems can greatly benefit from these technologies by giving scientists a better understanding of their data or model leading to new insights. While several projects have enabled web access to scientific workflow systems, they are primarily organized as a large portal server encapsulating the workflow engine. In this vision paper, we propose the design for Kepler WebView, a lightweight framework that integrates web technologies with the Kepler Scientific Workflow System. By embedding a web server in the Kepler process, Kepler WebView enables a wide variety of usage scenarios that would be difficult or impossible using the portal model. [ABSTRACT FROM AUTHOR]

We present a method to integrate multiple implementations of a machine learning algorithm in Kepler actors. This feature enables the user to compare accuracy and scalability of various implementations of a machine learning technique without having to change the workflow. These actors are based on the Execution Choice actor. They can be incorporated into any workflow to provide machine learning functionality. We describe a use case where actors that provide several implementations of k-means clustering can be used in a workflow to process sensor data from weather stations for predicting wildfire risks. [ABSTRACT FROM AUTHOR]

Scientific community across many disciplines is exploring new ways to extract knowledge from all available sources. Historically, written manuscripts have been the media of choice for recording experimental findings. Many disciplines such as social science, medical science are exploring ways to automate knowledge discovery from a vast repository of published scientific work. This work attempts to accelerate the process of information extraction by extending Kepler, a graphical workflow management tool. Kepler provides a simple way of designing and executing complex workflows in the form of directed graphs. This work presents a scalable approach to convert published research as PDF documents into indexable XML documents using Kepler. This conversion is a critical step in the Natural Language Processing pipeline. Kepler's distributed data processing capability enables scientists to scale this critical computation by simply adding more computing resources over the cloud. [ABSTRACT FROM AUTHOR]

Wildfires are critical for ecosystems in many geographical regions. However, our current urbanized existence in these environments is inducing the ecological balance to evolve into a different dynamic leading to the biggest fires in history. Wildfire wind speeds and directions change in an instant, and first responders can only be effective if they take action as quickly as the conditions change. What is lacking in disaster management today is a system integration of real-time sensor networks, satellite imagery, near-real time data management tools, wildfire simulation tools, and connectivity to emergency command centers before, during and after a wildfire. As a first time example of such an integrated system, the WIFIRE project is building an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. This paper summarizes the approach and early results of the WIFIRE project to integrate networked observations, e.g., heterogeneous satellite data and real-time remote sensor data with computational techniques in signal processing, visualization, modeling and data assimilation to provide a scalable, technological, and educational solution to monitor weather patterns to predict a wildfire's Rate of Spread. [ABSTRACT FROM AUTHOR]

Abstract: Scientific workflows integrate data and computing interfacesas configurable, semi-automatic graphs to solve a scientific problem. Kepler is such a software system for designing, executing, reusing, evolving, archiving and sharing scientific workflows. Electron tomography (ET) enables high-resolution views of complex cellular structures, such as cytoskeletons, organelles, viruses and chromosomes. Imaging investigations produce large datasets. For instance, in Electron Tomography, the size of a 16 fold image tilt series is about 65 Gigabytes with each projection image including 4096 by 4096 pixels. When we use serial sections or montage technique for large field ET, the dataset will be even larger. For higher resolution images with multiple tilt series, the data size may be in terabyte range. Demands of mass data processing and complex algorithms require the integration of diverse codes into flexible software structures. This paper describes a workflow for Electron Tomography Programs in Kepler (EPiK). This EPiKworkflow embeds the tracking process of IMOD, and realizes the main algorithms including filtered backprojection (FBP) from TxBR and iterative reconstruction methods. We have tested the three dimensional (3D) reconstruction process using EPiK on ET data. EPiK can be a potential toolkit for biology researchers with the advantage of logical viewing, easy handling, convenient sharing andfuture extensibility. [Copyright &y& Elsevier]

Abstract: We describe the development of automated workflows that support computed-aided drug discovery (CADD) and molecular dynamics (MD) simulations and are included as part of the National Biomedical Computation Resource (NBCR). The main workflow components include: file-management tasks, ligand force field parameterization, receptor-ligand molecular dynamics (MD) simulations, job submission, serial and parallel execution, and monitoring on relevant high-performance computing (HPC) resources, receptor structural clustering, virtual screening (VS), and statistical analyses of the VS results. The workflows aim to standardize simulation and analysis and promote best practices within the molecular simulation and CADD communities. Each component is developed as a stand-alone workflow, which should allow for easy integration into larger frameworks built suiting user needs, while remaining intuitive and easy to extend. [Copyright &y& Elsevier]

Abstract: With more and more workflow systems adopting cloud as their e xecution environment, it becomes increasingly challenging on how to effic iently manage various workflows, v irtual machines (VMs) and workflow e xecution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) a rchitecture with independent services. A core part of the architecture is how to efficiently respond continuous workflo w requests from users and schedule their e xecutions in the c loud. Based on diffe rent targets, we p ropose four heuristic workflo w scheduling a lgorith ms for the WFaaS architecture, and analy ze the differences and best usages of the algorith ms in terms of performance, cost and the price/performance ratio via experimental studies. [Copyright &y& Elsevier]

Abstract: Distributed Data-Parallel (DDP) patterns such as MapReduce have become increasingly popular as solutions to facilitate data-intensive applications, resulting in a number of systems supporting DDP workflows. Yet, applications or workflows built using these patterns are usually tightly-coupled with the underlying DDP execution engine they select. We present a framework for distributed data-parallel execution in the Kepler scientific workflow system that enables users to easily switch between different DDP execution engines. We describe a set of DDP actors based on DDP patterns and directors for DDP workflow executions within the presented framework. We demonstrate how DDP workflows can be easily composed in the Kepler graphic user interface through the reuse of these DDP actors and directors and how the generated DDP workflows can be executed in different distributed environments. Via a bioinformatics usecase, we discuss the usability of the proposed framework and validate its execution scalability. [Copyright &y& Elsevier]

Abstract: One of the greatest challenges in computational chemistry is the design of enzymes to catalyze non-natural chemical reactions. We focus on harnessing the distributed parallel computational power of the Grid to automate the inside-out process of enzyme design using scientific workflow systems. This paper presents a scientific workflow based approach to facilitate the inside-out enzyme design process in the Grid execution environment by providing features such as resource consolidation, task parallelism, provenance tracking, fault tolerance and workflow reuse, which results in an automated, pipelined, efficient, extensible, stable, and easy-to-use computational process for enzyme design. [ABSTRACT FROM AUTHOR]

Abstract: Environmental sensor networks are now commonly being deployed within environmental observatories and as components of smaller-scale ecological and environmental experiments. Effectively using data from these sensor networks presents technical challenges that are difficult for scientists to overcome, severely limiting the adoption of automated sensing technologies in environmental science. The Realtime Environment for Analytical Processing (REAP) is an NSF-funded project to address the technical challenges related to accessing and using heterogeneous sensor data from within the Kepler scientific workflow system. Using distinct use cases in terrestrial ecology and oceanography as motivating examples, we describe workflows and extensions to Kepler to stream and analyze data from observatory networks and archives. We focus on the use of two newly integrated data sources in Kepler: DataTurbine and OPeNDAP. Integrated access to both near real-time data streams and data archives from within Kepler facilitates both simple data exploration and sophisticated analysis and modeling with these data sources. [Copyright &y& Elsevier]