The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform

Caroline C. Friedel - One of the best experts on this subject based on the ideXlab platform.

  • Watchdog – a workflow management system for the distributed analysis of large-scale experimental data
    BMC Bioinformatics, 2018
    Co-Authors: Michael Kluge, Caroline C. Friedel
    Abstract:

    Background The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated Execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components. Results In this article, we present Watchdog , a WMS for the automated analysis of large-scale experimental data. Main features include straightforward processing of replicate data, support for distributed computer systems, customizable error detection and manual intervention into workflow Execution. Watchdog is implemented in Java and thus platform-independent and allows easy sharing of workflows and corresponding program modules. It provides a graphical user interface (GUI) for workflow construction using pre-defined modules as well as a helper script for creating new module definitions. Execution of workflows is possible using either the GUI or a command-line interface and a web-interface is provided for monitoring the Execution Status and intervening in case of errors. To illustrate its potentials on a real-life example, a comprehensive workflow and modules for the analysis of RNA-seq experiments were implemented and are provided with the software in addition to simple test examples. Conclusions Watchdog is a powerful and flexible WMS for the analysis of large-scale high-throughput experiments. We believe it will greatly benefit both users with and without programming skills who want to develop and apply bioinformatical workflows with reasonable overhead. The software, example workflows and a comprehensive documentation are freely available at www.bio.ifi.lmu.de/watchdog.

  • Watchdog – a workflow management system for the distributed analysis of large-scale experimental data
    BMC bioinformatics, 2018
    Co-Authors: Michael Kluge, Caroline C. Friedel
    Abstract:

    The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated Execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components. In this article, we present Watchdog, a WMS for the automated analysis of large-scale experimental data. Main features include straightforward processing of replicate data, support for distributed computer systems, customizable error detection and manual intervention into workflow Execution. Watchdog is implemented in Java and thus platform-independent and allows easy sharing of workflows and corresponding program modules. It provides a graphical user interface (GUI) for workflow construction using pre-defined modules as well as a helper script for creating new module definitions. Execution of workflows is possible using either the GUI or a command-line interface and a web-interface is provided for monitoring the Execution Status and intervening in case of errors. To illustrate its potentials on a real-life example, a comprehensive workflow and modules for the analysis of RNA-seq experiments were implemented and are provided with the software in addition to simple test examples. Watchdog is a powerful and flexible WMS for the analysis of large-scale high-throughput experiments. We believe it will greatly benefit both users with and without programming skills who want to develop and apply bioinformatical workflows with reasonable overhead. The software, example workflows and a comprehensive documentation are freely available at www.bio.ifi.lmu.de/watchdog.

Michael Kluge - One of the best experts on this subject based on the ideXlab platform.

  • Watchdog – a workflow management system for the distributed analysis of large-scale experimental data
    BMC Bioinformatics, 2018
    Co-Authors: Michael Kluge, Caroline C. Friedel
    Abstract:

    Background The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated Execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components. Results In this article, we present Watchdog , a WMS for the automated analysis of large-scale experimental data. Main features include straightforward processing of replicate data, support for distributed computer systems, customizable error detection and manual intervention into workflow Execution. Watchdog is implemented in Java and thus platform-independent and allows easy sharing of workflows and corresponding program modules. It provides a graphical user interface (GUI) for workflow construction using pre-defined modules as well as a helper script for creating new module definitions. Execution of workflows is possible using either the GUI or a command-line interface and a web-interface is provided for monitoring the Execution Status and intervening in case of errors. To illustrate its potentials on a real-life example, a comprehensive workflow and modules for the analysis of RNA-seq experiments were implemented and are provided with the software in addition to simple test examples. Conclusions Watchdog is a powerful and flexible WMS for the analysis of large-scale high-throughput experiments. We believe it will greatly benefit both users with and without programming skills who want to develop and apply bioinformatical workflows with reasonable overhead. The software, example workflows and a comprehensive documentation are freely available at www.bio.ifi.lmu.de/watchdog.

  • Watchdog – a workflow management system for the distributed analysis of large-scale experimental data
    BMC bioinformatics, 2018
    Co-Authors: Michael Kluge, Caroline C. Friedel
    Abstract:

    The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated Execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components. In this article, we present Watchdog, a WMS for the automated analysis of large-scale experimental data. Main features include straightforward processing of replicate data, support for distributed computer systems, customizable error detection and manual intervention into workflow Execution. Watchdog is implemented in Java and thus platform-independent and allows easy sharing of workflows and corresponding program modules. It provides a graphical user interface (GUI) for workflow construction using pre-defined modules as well as a helper script for creating new module definitions. Execution of workflows is possible using either the GUI or a command-line interface and a web-interface is provided for monitoring the Execution Status and intervening in case of errors. To illustrate its potentials on a real-life example, a comprehensive workflow and modules for the analysis of RNA-seq experiments were implemented and are provided with the software in addition to simple test examples. Watchdog is a powerful and flexible WMS for the analysis of large-scale high-throughput experiments. We believe it will greatly benefit both users with and without programming skills who want to develop and apply bioinformatical workflows with reasonable overhead. The software, example workflows and a comprehensive documentation are freely available at www.bio.ifi.lmu.de/watchdog.

Wei Tan - One of the best experts on this subject based on the ideXlab platform.

  • Tablet-based Information System for Commercial Air-craft: Onboard Context-Sensitive Information System (OCSIS).
    arXiv: Human-Computer Interaction, 2018
    Co-Authors: Guy A. Boy, Wei Tan
    Abstract:

    Pilots currently use paper-based documentation and electronic systems to help them perform procedures to ensure safety, efficiency and comfort on commercial aircrafts. Management of interconnections among paper-based operational documents can be a challenge for pilots, especially when time pressure is high in normal, abnormal, and emergency situations. This dissertation is a contribution to the design of an Onboard Context-Sensitive Information System (OCSIS), which was developed on a tablet. The claim is that the use of con-textual information facilitates access to appropriate operational content at the right time either automatically or on demand. OCSIS was tested using human-in-the-loop simulations that involved professional pilots in the Airbus 320 cockpit simulator. First results are encouraging that show OCSIS can be usable and useful for operational information access. More specifically, context-sensitivity contributes to simplify this access (i.e., appropriate operational information is provided at the right time in the right format. In addition, OCSIS provides other features that paper-based documents do not have, such as procedure Execution Status after an interruption. Also, the fact that several calculations are automatically done by OCSIS tends to decrease the pilot's task demand .

  • HCI (6) - Tablet-Based Information System for Commercial Aircraft: Onboard Context-Sensitive Information System (OCSIS)
    Engineering Psychology and Cognitive Ergonomics, 2018
    Co-Authors: Wei Tan, Guy A. Boy
    Abstract:

    Pilots currently use paper-based documentation and electronic systems to help them perform procedures to ensure safety, efficiency and comfort on commercial aircrafts. Management of interconnections among paper-based operational documents can be a challenge for pilots, especially when time pressure is high in normal, abnormal, and emergency situations. This dissertation is a contribution to the design of an Onboard Context-Sensitive Information System (OCSIS), which was developed on a tablet. The claim is that the use of contextual information facilitates access to appropriate operational content at the right time either automatically or on demand. OCSIS was tested using human-in-the-loop simulations that involved professional pilots in the Airbus 320 cockpit simulator. First results are encouraging that show OCSIS can be usable and useful for operational information access. More specifically, context-sensitivity contributes to simplify this access (i.e., appropriate operational information is provided at the right time in the right format. In addition, OCSIS provides other features that paper-based documents do not have, such as procedure Execution Status after an interruption. Also, the fact that several calculations are automatically done by OCSIS tends to decrease the pilot’s task demand.

Michal Pelczar - One of the best experts on this subject based on the ideXlab platform.

  • eScience - From Monitoring Data to Experiment Information Monitoring of Grid Scientific Workflows
    Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007), 2007
    Co-Authors: Bartosz Balis, Marian Bubak, Michal Pelczar
    Abstract:

    Monitoring of running scientific workflows (experiments) is not only important for observing their Execution Status, but also for collecting provenance, improving performance, knowledge extraction, etc. We propose an ontology model of experiment information which describes the Execution of an experiment using a well-defined semantics, and aggregates various aspects of workflow Execution including provenance, performance, resource information, and others. Such multi-aspect semantic-rich information is indispensable to build knowledge services on top of it. We describe a grid workflow monitoring architecture which is necessary to collect and correlate workflow monitoring data. The process of aggregation of monitoring data into experiment information is presented. Our approach is validated on a drug resistance ranking application running in the ViroLab virtual laboratory for infectious diseases.

Schahram Dustdar - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic Instrumentation, Performance Monitoring and Analysis of Grid Scientific Workflows
    Journal of Grid Computing, 2005
    Co-Authors: Hong-linh Truong, Thomas Fahringer, Schahram Dustdar
    Abstract:

    While existing work concentrates on developing QoS models of business workflows and Web services, few tools have been developed to support the monitoring and performance analysis of scientific workflows in Grids. This paper describes novel Grid services for dynamic instrumentation of Grid-based applications, performance monitoring and analysis of Grid scientific workflows. We describe a Grid dynamic instrumentation service that provides a widely accessible interface for other services and users to conduct the dynamic instrumentation of Grid applications during the runtime. We introduce a Grid performance analysis service for Grid scientific workflows. The analysis service utilizes various types of data including workflow graphs, monitoring data of resources, Execution Status of activities, and performance measurements obtained from the dynamic instrumentation of invoked applications, and provides a rich set of functionalities and features to support the online monitoring and performance analysis of scientific workflows. Workflows and their relevant information including performance metrics are stored and utilized for comparing the performance of constructs of different workflows and for supporting multi-workflow analysis.