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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]

This paper extends FARSITE (a software used for wildfire modeling and simulation) to incorporate data assimilation techniques based on noisy and limited spatial resolution observations of the fire perimeter to improve the accuracy of wildfire spread predictions. To include data assimilation in FARSITE, uncertainty on both the simulated wildfire perimeter and the measured wildfire perimeter is used to formulate optimal updates for the prediction of the spread of the wildfire. For data assimilation, Wildfire perimeter measurements with limited spatial resolution and a known uncertainty are used to to formulate an optimal adjustment in the fire perimeter prediction. The adjustment is calculated from the Kalman filter gain in an Ensemble Kalman filter that exploit the uncertainty information on both the simulated wildfire perimeter and the measured wildfire perimeter. The approach is illustated on a wildfire simulation representing the 2014 Cocos fire and presents comparison results for hourly data assimilation results. [ABSTRACT FROM AUTHOR]

As more and more public and private Cloud resources are becoming available, it is common for a user to have access to multiple Cloud resources at the same time. Cloud federation dynamically aggregates multiple Cloud resources into a federated one. This paper explores how to build and run scientific workflows on top of a federated Cloud by integrating Kepler scientific workflow platform with CometCloud platform. Our integration can leverage capabilities of the two plat- forms: 1) dynamic resource federation, provisioning and allocation from CometCloud; 2) Easy workflow composition from Kepler; 3) Dynamic workflow scheduling and execution from the integration. We apply our integration to a bioinformatics workflow with three Cloud resources to evaluate its capabilities. We also discuss possible future directions from the integration. [ABSTRACT FROM AUTHOR]

The BBDTC ( https://biobigdata.ucsd.edu ) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC collaborative is an e-learning platform that supports the biomedical community to access, develop and deploy open training materials. The BBDTC supports Big Data skill training for biomedical scientists at all levels, and from varied backgrounds. The natural hierarchy of courses allows them to be broken into and handled as modules. Modules can be reused in the context of multiple courses and reshuffled, producing a new and different, dynamic course called a playlist. Users may create playlists to suit their learning requirements and share it with individual users or the wider public. BBDTC leverages the maturity and design of the HUBzero content-management platform for delivering educational content. To facilitate the migration of existing content, the BBDTC supports importing and exporting course material from the edX platform. Migration tools will be extended in the future to support other platforms. Hands-on training software packages, i.e., toolboxes, are supported through Amazon EC2 and Virtualbox virtualization technologies, and they are available as: (i) downloadable lightweight Virtualbox Images providing a standardized software tool environment with software packages and test data on their personal machines, and (ii) remotely accessible Amazon EC2 Virtual Machines for accessing biomedical big data tools and scalable big data experiments. At the moment, the BBDTC site contains three open Biomedical big data training courses with lecture contents, videos and hands-on training utilizing VM toolboxes, covering diverse topics. The courses have enhanced the hands-on learning environment by providing structured content that users can use at their own pace. A four course biomedical big data series is planned for development in early 2016. [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: 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]