31 results on '"Crawl, Daniel"'
Search Results
2. Firemap: A Dynamic Data-Driven Predictive Wildfire Modeling and Visualization Environment
- Author
-
Crawl, Daniel, Block, Jessica, Lin, Kai, and Altintas, Ilkay
- Published
- 2017
- Full Text
- View/download PDF
3. Multimodal Wildland Fire Smoke Detection
- Author
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Baldota, Siddhant, Ramaprasad, Shreyas Anantha, Bhamra, Jaspreet Kaur, Luna, Shane, Ramachandra, Ravi, Zen, Eugene, Kim, Harrison, Crawl, Daniel, Perez, Ismael, Altintas, Ilkay, Cottrell, Garrison W., and Nguyen, Mai H.
- Subjects
FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.
- Published
- 2022
4. Progress towards Automated Kepler Scientific Workflows for Computer-aided Drug Discovery and Molecular Simulations
- Author
-
Ieong, Pek U., Sørensen, Jesper, Vemu, Prasantha L., Wong, Celia W., Demir, Özlem, Williams, Nadya P., Wang, Jianwu, Crawl, Daniel, Swift, Robert V., Malmstrom, Robert D., Altintas, Ilkay, and Amaro, Rommie E.
- Published
- 2014
- Full Text
- View/download PDF
5. Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms
- Author
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Wang, Jianwu, Korambath, Prakashan, Altintas, Ilkay, Davis, Jim, and Crawl, Daniel
- Published
- 2014
- Full Text
- View/download PDF
6. Multimodal Wildland Fire Smoke Detection.
- Author
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Bhamra, Jaspreet Kaur, Anantha Ramaprasad, Shreyas, Baldota, Siddhant, Luna, Shane, Zen, Eugene, Ramachandra, Ravi, Kim, Harrison, Schmidt, Chris, Arends, Chris, Block, Jessica, Perez, Ismael, Crawl, Daniel, Altintas, Ilkay, Cottrell, Garrison W., and Nguyen, Mai H.
- Subjects
WILDFIRES ,FIRE detectors ,WILDFIRE prevention ,SMOKE ,DEEP learning ,OPTICAL images ,WILDFIRE risk - Abstract
Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
7. A Framework for Distributed Data-Parallel Execution in the Kepler Scientific Workflow System
- Author
-
Wang, Jianwu, Crawl, Daniel, and Altintas, Ilkay
- Published
- 2012
- Full Text
- View/download PDF
8. Recursive Updates of Wildfire Perimeters Using Barrier Points and Ensemble Kalman Filtering
- Author
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Subramanian, Abhishek, Tan, Li, de Callafon, Raymond A., Crawl, Daniel, and Altintas, Ilkay
- Subjects
Barrier points ,farsite ,Ensemble Kalman filter ,Wildfire ,Ensembles ,Physics::Atmospheric and Oceanic Physics ,Article - Abstract
This paper shows how the wildfire simulation tool farsite is augmented with data assimilation capabilities that exploit the notion of barrier points and a constraint-point ensemble Kalman filtering to update wildfire perimeter predictions. Based on observations of the actual fire perimeter, stationary points on the fire perimeter are identified as barrier points and combined with a recursive update of the initial fire perimeter. It is shown that the combination of barrier point identification and using the barrier points as constraints in the ensemble Kalman filter gives a significant improvement in the forward prediction of the fire perimeter. The results are illustrated on the use case of the 2016 Sandfire that burned in the Angeles National Forest, east of the Santa Clarita Valley in Los Angeles County, California.
- Published
- 2020
9. Theoretical enzyme design using the Kepler scientific workflows on the Grid
- Author
-
Wang, Jianwu, Korambath, Prakashan, Kim, Seonah, Johnson, Scott, Jin, Kejian, Crawl, Daniel, Altintas, Ilkay, Smallen, Shava, Labate, Bill, and Houk, Kendall N.
- Published
- 2010
- Full Text
- View/download PDF
10. Modular Resource Centric Learning for Workflow Performance Prediction
- Author
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Singh, Alok, Nguyen, Mai, Purawat, Shweta, Crawl, Daniel, and Altintas, Ilkay
- Subjects
FOS: Computer and information sciences ,Computer Science - Learning ,Computer Science - Distributed, Parallel, and Cluster Computing ,Distributed, Parallel, and Cluster Computing (cs.DC) ,Machine Learning (cs.LG) - Abstract
Workflows provide an expressive programming model for fine-grained control of large-scale applications in distributed computing environments. Accurate estimates of complex workflow execution metrics on large-scale machines have several key advantages. The performance of scheduling algorithms that rely on estimates of execution metrics degrades when the accuracy of predicted execution metrics decreases. This in-progress paper presents a technique being developed to improve the accuracy of predicted performance metrics of large-scale workflows on distributed platforms. The central idea of this work is to train resource-centric machine learning agents to capture complex relationships between a set of program instructions and their performance metrics when executed on a specific resource. This resource-centric view of a workflow exploits the fact that predicting execution times of sub-modules of a workflow requires monitoring and modeling of a few dynamic and static features. We transform the input workflow that is essentially a directed acyclic graph of actions into a Physical Resource Execution Plan (PREP). This transformation enables us to model an arbitrarily complex workflow as a set of simpler programs running on physical nodes. We delegate a machine learning model to capture performance metrics for each resource type when it executes different program instructions under varying degrees of resource contention. Our algorithm takes the prediction metrics from each resource agent and composes the overall workflow performance metrics by utilizing the structure of the corresponding Physical Resource Execution Plan., This paper was presented at: 6th Workshop on Big Data Analytics: Challenges, and Opportunities (BDAC) at the 27th IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2015)
- Published
- 2017
11. Toward a Methodology and Framework for Workflow-Driven Team Science.
- Author
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Altintas, Ilkay, Purawat, Shweta, Crawl, Daniel, Singh, Alok, and Marcus, Kyle
- Subjects
WORKFLOW ,CYBERINFRASTRUCTURE ,CONCEPTUAL design ,DYNAMICAL systems ,TEAMS - Abstract
Scientific workflows are powerful tools for the management of scalable experiments, often composed of complex tasks running on distributed resources. Existing cyberinfrastructure provides components that can be utilized within repeatable workflows. However, data and computing advances continuously change the way scientific workflows get developed and executed, pushing the scientific activity to be more data-driven, heterogeneous, and collaborative. Workflow development today depends on the effective collaboration and communication of a cross-disciplinary team, not only with humans but also with analytical systems and infrastructure. This paper presents a collaboration-centered reference architecture to extend workflow systems with dynamic, predictable, and programmable interfaces to systems and infrastructure while bridging the exploratory and scalable activities in the scientific process. We present a conceptual design toward the development of methodologies and services for effective workflow-driven collaborations, namely the PPoDS methodology for collaborative workflow development and the SmartFlows Services for smart execution in a rapidly evolving cyberinfrastructure ecosystem. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
12. Big data provenance: Challenges, state of the art and opportunities.
- Author
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Wang, Jianwu, Crawl, Daniel, Purawat, Shweta, Nguyen, Mai, and Altintas, Ilkay
- Published
- 2015
- Full Text
- View/download PDF
13. Kepler WebView: A Lightweight, Portable Framework for Constructing Real-time Web Interfaces of Scientific Workflows.
- Author
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Crawl, Daniel, Singh, Alok, and Altintas, Ilkay
- Subjects
DATA modeling ,WORKFLOW management systems ,COMPUTER engineering ,COMPUTER science - Abstract
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]
- Published
- 2016
- Full Text
- View/download PDF
14. Integrated Machine Learning in the Kepler Scientific Workflow System.
- Author
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Nguyen, Mai, Crawl, Daniel, Masoumi, Tahereh, and Altintas, Ilkay
- Subjects
KEPLER'S equation ,MACHINE learning ,WORKFLOW ,WILDFIRE risk ,SCALABILITY ,K-means clustering - Abstract
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]
- Published
- 2016
- Full Text
- View/download PDF
15. Natural Language Processing using Kepler Workflow System: First Steps.
- Author
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Goyal, Ankit, Singh, Alok, Bhargava, Shitij, Crawl, Daniel, Altintas, Ilkay, and Hsu, Chun-Nan
- Subjects
NATURAL language processing ,KEPLER problem ,WORKFLOW management systems ,SCIENTIFIC community ,DATA mining - Abstract
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]
- Published
- 2016
- Full Text
- View/download PDF
16. Towards an Integrated Cyberinfrastructure for Scalable Data-driven Monitoring, Dynamic Prediction and Resilience of Wildfires.
- Author
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Altintas, Ilkay, Block, Jessica, de Callafon, Raymond, Crawl, Daniel, Cowart, Charles, Gupta, Amarnath, Nguyen, Mai, Braun, Hans-Werner, Schulze, Jurgen, Gollner, Michael, Trouve, Arnaud, and Smarr, Larry
- Subjects
WILDFIRES ,STATISTICAL decision making ,PREDICTION models ,CYBERINFRASTRUCTURE ,URBANIZATION ,WIND speed - Abstract
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]
- Published
- 2015
- Full Text
- View/download PDF
17. Challenges and approaches for distributed workflow-driven analysis of large-scale biological data.
- Author
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Altintas, Ilkay, Wang, Jianwu, Crawl, Daniel, and Li, Weizhong
- Published
- 2012
- Full Text
- View/download PDF
18. Provenance for MapReduce-based data-intensive workflows.
- Author
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Crawl, Daniel, Wang, Jianwu, and Altintas, Ilkay
- Published
- 2011
- Full Text
- View/download PDF
19. Monitoring data quality in Kepler.
- Author
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Na'im, Aisa, Crawl, Daniel, Indrawan, Maria, Altintas, Ilkay, and Sun, Shulei
- Published
- 2010
- Full Text
- View/download PDF
20. A Fault-Tolerance Architecture for Kepler-Based Distributed Scientific Workflows.
- Author
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Mouallem, Pierre, Crawl, Daniel, Altintas, Ilkay, Vouk, Mladen, and Yildiz, Ustun
- Abstract
Fault-tolerance and failure recovery in scientific workflows is still a relatively young topic. The work done in the domain so far mostly applies classic fault-tolerance mechanisms, such as "alternative versions" and "checkpointing", to scientific workflows. Often scientific workflow systems simply rely on the fault-tolerance capabilities provided by their third party subcomponents such as schedulers, Grid resources, or the underlying operating systems. When failures occur at the underlying layers, a workflow system typically sees them only as failed steps in the process without additional detail and the ability of the system to recover from those failures may be limited. In this paper, we present an architecture that tries to address this for Kepler-based scientific workflows by providing more information about failures and faults we have observed, and through a supporting implementation of more comprehensive failure coverage and recovery options. We discuss our framework in the context of the failures observed in two production-level Kepler-based workflows, specifically XGC and S3D. The framework is divided into three major components: (i) a general contingency Kepler actor that provides a recovery block functionality at the workflow level, (ii) an external monitoring module that tracks the underlying workflow components, and monitors the overall health of the workflow execution, and (iii) a checkpointing mechanism that provides smart resume capabilities for cases in which an unrecoverable error occurs. This framework takes advantage of the provenance data collected by the Kepler-based workflows to detect failures and help in fault-tolerance decision making. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
21. Understanding Collaborative Studies through Interoperable Workflow Provenance.
- Author
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Altintas, Ilkay, Anand, Manish Kumar, Crawl, Daniel, Bowers, Shawn, Belloum, Adam, Missier, Paolo, Ludäscher, Bertram, Goble, Carole A., and Sloot, Peter M. A.
- Abstract
The provenance of a data product contains information about how the product was derived, and is crucial for enabling scientists to easily understand, reproduce, and verify scientific results. Currently, most provenance models are designed to capture the provenance related to a single run, and mostly executed by a single user. However, a scientific discovery is often the result of methodical execution of many scientific workflows with many datasets produced at different times by one or more users. Further, to promote and facilitate exchange of information between multiple workflow systems supporting provenance, the Open Provenance Model (OPM) has been proposed by the scientific workflow community. In this paper, we describe a new query model that captures implicit user collaborations. We show how this model maps to OPM and helps to answer collaborative queries, e.g., identifying combined workflows and contributions of users collaborating on a project based on the records of previous workflow executions. We also adopt and extend the high-level Query Language for Provenance (QLP) with additional constructs, and show how these extensions allow non-expert users to express collaborative provenance queries against this model easily and concisely. Furthermore, we adopt the Provenance Challenge 3 (PC3) workflows as a collaborative and interoperable usecase scenario, where different stages of the workflow are executed in three different workflow environments - Kepler, Taverna, and WSVLAM. Through this usecase, we demonstrate how we can establish and understand collaborative studies through interoperable workflow provenance. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
22. Kepler + Hadoop.
- Author
-
Wang, Jianwu, Crawl, Daniel, and Altintas, Ilkay
- Published
- 2009
- Full Text
- View/download PDF
23. A Provenance-Based Fault Tolerance Mechanism for Scientific Workflows.
- Author
-
Crawl, Daniel and Altintas, Ilkay
- Abstract
Capturing provenance information in scientific workflows is not only useful for determining data-dependencies, but also for a wide range of queries including fault tolerance and usage statistics. As collaborative scientific workflow environments provide users with reusable shared workflows, collection and usage of provenance data in a generic way that could serve multiple data and computational models become vital. This paper presents a method for capturing data value- and control- dependencies for provenance information collection in the Kepler scientific workflow system. It also describes how the collected information based on these dependencies could be used for a fault tolerance framework in different models of computation. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
24. Using Location Dependence to Manage Mobile Data.
- Author
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Crawl, Daniel, Dunn, Joseph, Bennett, John, Bhatnagar, Avneesh, and Speight, Evan
- Published
- 2006
- Full Text
- View/download PDF
25. Big Data Applications Using Workflows for Data Parallel Computing.
- Author
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Wang, Jianwu, Crawl, Daniel, Altintas, Ilkay, and Li, Weizhong
- Subjects
DATA analysis ,BIG data ,DATA science ,ELECTRONIC data processing ,DATA mining - Abstract
In the Big Data era, workflow systems need to embrace data parallel computing techniques for efficient data analysis and analytics. Here, the authors present an easy-to-use, scalable approach to build and execute Big Data applications using actor-oriented modeling in data parallel computing. They use two bioinformatics use cases for next-generation sequencing data analysis to verify the feasibility of their approach. [ABSTRACT FROM PUBLISHER]
- Published
- 2014
- Full Text
- View/download PDF
26. EPiK-a Workflow for Electron Tomography in Kepler 1.
- Author
-
Chen, Ruijuan, Wan, Xiaohua, Altintas, Ilkay, Wang, Jianwu, Crawl, Daniel, Phan, Sébastien, Lawrence, Albert, and Ellisman, Mark
- Subjects
TOMOGRAPHY ,WORKFLOW management ,DATA analysis ,COMPUTER software ,INFORMATION sharing ,COMPUTATIONAL complexity - Abstract
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]
- Published
- 2014
- Full Text
- View/download PDF
27. Approaches to Distributed Execution of Scientific Workflows in Kepler.
- Author
-
Płóciennik, Marcin, Żok, Tomasz, Altintas, Ilkay, Wang, Jianwu, Crawl, Daniel, Abramson, David, Imbeaux, Frederic, Guillerminet, Bernard, Lopez-Caniego, Marcos, Plasencia, Isabel Campos, Pych, Wojciech, Ciecieląg, Pawel, Palak, Bartek, Owsiak, Michał, and Frauel, Yann
- Subjects
DISTRIBUTION (Probability theory) ,WORKFLOW software ,COMPUTER systems ,COMPARATIVE studies ,DATA analysis ,KEPLER'S equation ,STRATOSPHERE ,COMPUTATIONAL chemistry ,APPLICATION software - Abstract
The Kepler scientific workflow system enables creation, execution and sharing of workflows across a broad range of scientific and engineering disciplines while also facilitating remote and distributed execution of workflows. In this paper, we present and compare different approaches to distributed execution of workflows using the Kepler environment, including a distributed data-parallel framework using Hadoop and Stratosphere, and Cloud and Grid execution using Serpens, Nimrod/K and Globus actors. We also present real-life applications in computational chemistry, bioinformatics and computational physics to demonstrate the usage of different distributed computing capabilities of Kepler in executable workflows. We further analyze the differences of each approach and provide a guidance for their applications. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
28. Workflows and extensions to the Kepler scientific workflow system to support environmental sensor data access and analysis.
- Author
-
Barseghian, Derik, Altintas, Ilkay, Jones, Matthew B., Crawl, Daniel, Potter, Nathan, Gallagher, James, Cornillon, Peter, Schildhauer, Mark, Borer, Elizabeth T., Seabloom, Eric W., and Hosseini, Parviez R.
- Subjects
SENSOR networks ,WORKFLOW software ,ECOLOGICAL research ,INFORMATION processing ,OCEANOGRAPHY ,DATA libraries - Abstract
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]
- Published
- 2010
- Full Text
- View/download PDF
29. Estimation of wildfire wind conditions via perimeter and surface area optimization.
- Author
-
Tan, Li, de Callafon, Raymond A., Block, Jessica, Crawl, Daniel, Çağlar, Tolga, and Altıntaş, Ilkay
- Subjects
SURFACE area ,WILDFIRES ,WILDFIRE prevention ,WIND speed - Abstract
This paper shows that the prediction capability of wildfire progression can be improved by estimation of a single prevailing wind vector parametrized by a wind speed and a wind direction to drive a wildfire simulation created by FARSITE. Estimations of these wind vectors are achieved in this work by a gradient-free optimization via a grid search that compares wildfire model simulations with measured wildfire perimeters, where noisy observations are modeled as uncertainties on the locations of the vertices of the measured wildfire perimeters. Two optimizations are established to acquire the optimal wind speed and wind direction. To formulate a perimeter optimization, an uncertainty-weighted least-squares error is computed between the vertices of the simulated and measured wildfire perimeter. The challenge in this approach is to match the number of vertices on the simulated and measured wildfire perimeter via interpolation of perimeter points and their uncertainties. For a surface area optimization, an uncertainty-weighted surface area error is introduced to capture the surface of the union minus the intersection of the simulated and measured wildfire perimeter. The challenge in this approach is to formulate a surface area error, weighted by the uncertainties on the locations of the vertices of the measured wildfire perimeter. The optimization in this paper is based on an iterative refinement of a grid of the wind vector and provides robustness to intermittent erroneous results produced by FARSITE, while allowing parallel execution of wildfire model calculations. This paper is an extension of the work in Tan et al., (2021). Results on wind vector estimation are illustrated on two historical wildfire events: the 2019 Maria Fire that burned south of the community of Santa Paula in the area of Somis, CA, and the 2019 Cave Fire that started in the Santa Ynez Mountains of Santa Barbara County. • Improve prediction capability of wildfire progression by estimating the predominant wind conditions. • Perimeter optimization and surface area optimization are introduced for the estimation of wind conditions. • Numerical results are illustrated on two historical wildfire events, Maria Fire and Cave Fire in California. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
30. Big Data Provenance: Challenges, State of the Art and Opportunities.
- Author
-
Wang J, Crawl D, Purawat S, Nguyen M, and Altintas I
- Abstract
Ability to track provenance is a key feature of scientific workflows to support data lineage and reproducibility. The challenges that are introduced by the volume, variety and velocity of Big Data, also pose related challenges for provenance and quality of Big Data, defined as veracity. The increasing size and variety of distributed Big Data provenance information bring new technical challenges and opportunities throughout the provenance lifecycle including recording, querying, sharing and utilization. This paper discusses the challenges and opportunities of Big Data provenance related to the veracity of the datasets themselves and the provenance of the analytical processes that analyze these datasets. It also explains our current efforts towards tracking and utilizing Big Data provenance using workflows as a programming model to analyze Big Data.
- Published
- 2015
- Full Text
- View/download PDF
31. EPiK-a Workflow for Electron Tomography in Kepler.
- Author
-
Chen R, Wan X, Altintas I, Wang J, Crawl D, Phan S, Lawrence A, and Ellisman M
- Abstract
Scientific workflows integrate data and computing interfaces as 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 EPiK workflow 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 and future extensibility.
- Published
- 2014
- Full Text
- View/download PDF
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