The Experts below are selected from a list of 147 Experts worldwide ranked by ideXlab platform

Zenon Starčuk - One of the best experts on this subject based on the ideXlab platform.

  • Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance
    BMC Bioinformatics, 2017
    Co-Authors: Michał Jabłoński, Jana Starcukova, Zenon Starčuk
    Abstract:

    BackgroundProton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and File Identification.ResultsA database storing all processing steps, parameters and Files used in processing was developed for jMRUI. The solution was developed in Java, authors used a SQL database for robust storage of parameters and SHA-256 hash code for unambiguous File Identification. The developed system was integrated directly in jMRUI and it will be publically available. A graphical user interface was implemented in order to make the user experience more comfortable. The database operation is invisible from the point of view of the common user, all tracking operations are performed in the background.ConclusionsThe implemented jMRUI database is a tool that can significantly help the user to track the processing history performed on data in jMRUI. The created tool is oriented to be user-friendly, robust and easy to use. The database GUI allows the user to browse the whole processing history of a selected File and learn e.g. what processing lead to the results, where the original data are stored, to obtain the list of all processing actions performed on spectra.

  • Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance.
    BMC Bioinformatics, 2017
    Co-Authors: Michal Jablonski, Jana Starcukova, Zenon Starčuk
    Abstract:

    Background Proton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and File Identification.

Carlos Maltzahn - One of the best experts on this subject based on the ideXlab platform.

  • QMDS: A File System Metadata Management Service Supporting a Graph Data Model-Based Query Language
    2011 IEEE Sixth International Conference on Networking Architecture and Storage, 2011
    Co-Authors: Sasha Ames, Maya B. Gokhale, Carlos Maltzahn
    Abstract:

    File system metadata management has become a bottleneck for many data-intensive applications that rely on high-performance File systems. Part of the bottleneck is due to the limitations of an almost 50 year old interface standard with metadata abstractions that were designed at a time when high-end File systems managed less than 100 MB. Today's high-performance File systems store 7 to 9 orders of magnitude more data, resulting in numbers of data items for which these metadata abstractions are inadequate, such as directory hierarchies unable to handle complex relationships among data. Users of File systems have attempted to work around these inadequacies by moving application-specific metadata management to relational databases to make metadata searchable. Splitting File system metadata management into two separate systems introduces inefficiencies and systems management problems. To address this problem, we propose QMDS: a File system metadata management service that integrates all File system metadata and uses a graph data model with attributes on nodes and edges. Our service uses a query language interface for File Identification and attribute retrieval. We present our metadata management service design and architecture and study its performance using a text analysis benchmark application. Results from our QMDS prototype show the effectiveness of this approach. Compared to the use of a File system and relational database, the QMDS prototype shows superior performance for both ingest and query workloads.

  • NAS - QMDS: A File System Metadata Management Service Supporting a Graph Data Model-Based Query Language
    2011 IEEE Sixth International Conference on Networking Architecture and Storage, 2011
    Co-Authors: Sasha Ames, Maya Gokhale, Carlos Maltzahn
    Abstract:

    File system metadata management has become a bottleneck for many data-intensive applications that rely on high-performance File systems. Part of the bottleneck is due to the limitations of an almost 50 year old interface standard with metadata abstractions that were designed at a time when high-end File systems managed less than 100 MB. Today's high-performance File systems store 7 to 9 orders of magnitude more data, resulting in numbers of data items for which these metadata abstractions are inadequate, such as directory hierarchies unable to handle complex relationships among data. Users of File systems have attempted to work around these inadequacies by moving application-specific metadata management to relational databases to make metadata searchable. Splitting File system metadata management into two separate systems introduces inefficiencies and systems management problems. To address this problem, we propose QMDS: a File system metadata management service that integrates all File system metadata and uses a graph data model with attributes on nodes and edges. Our service uses a query language interface for File Identification and attribute retrieval. We present our metadata management service design and architecture and study its performance using a text analysis benchmark application. Results from our QMDS prototype show the effectiveness of this approach. Compared to the use of a File system and relational database, the QMDS prototype shows superior performance for both ingest and query workloads.

  • Efficient, searchable, graph-structured File system metadata services
    2011
    Co-Authors: Carlos Maltzahn, Alexander K. Ames
    Abstract:

    File system metadata management has become a bottleneck for many data-intensive applications that rely on high-performance File systems. Part of the bottleneck is due to the limitations of an almost 50 year old interface standard with metadata abstractions that were designed at a time when high-end File systems managed less than 100MB. Today's high-performance File systems store 7 to 9 orders of magnitude more data, resulting in numbers of data items for which these metadata abstractions are inadequate, such as directory hierarchies unable to handle complex relationships among data. Users of File systems have attempted to work around these inadequacies by moving application-specific metadata management to relational databases to make metadata searchable. Splitting File system metadata management into two separate systems introduces inefficiencies and systems management problems. To address the problem, we propose QMDS: a File system metadata management service that integrates all File system metadata and uses a graph data model with attributes on nodes and edges. This dissertation explores the effectiveness of this approach. We present the data model, a query language interface, the design of a prototype metadata store and query processing. Our graph-based logical data model extends the hierarchical model already in use for File systems. Hierarchies are inadequate for the organizational needs of several example domains, but we show that the graph model can support their needs. The query language interface allows for File Identification and attribute retrieval, based on graph-oriented search operators instead of relational table oriented joins. The prototype design uses in-memory based data structures within an architecture that uses memory-mapped Files for persistent metadata storage. We use workloads from three example domains to evaluate the prototype based on ingest and query performance. Notably, within one of our workloads, when compared to the use of a File system and relational database, the QMDS prototype shows superior performance for both ingest and query workloads. Finally, we contrast the static properties and access patterns from these three workloads to explore the effectiveness of our design choices and suggest options for system and hardware configurations.

Michał Jabłoński - One of the best experts on this subject based on the ideXlab platform.

  • Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance
    BMC Bioinformatics, 2017
    Co-Authors: Michał Jabłoński, Jana Starcukova, Zenon Starčuk
    Abstract:

    BackgroundProton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and File Identification.ResultsA database storing all processing steps, parameters and Files used in processing was developed for jMRUI. The solution was developed in Java, authors used a SQL database for robust storage of parameters and SHA-256 hash code for unambiguous File Identification. The developed system was integrated directly in jMRUI and it will be publically available. A graphical user interface was implemented in order to make the user experience more comfortable. The database operation is invisible from the point of view of the common user, all tracking operations are performed in the background.ConclusionsThe implemented jMRUI database is a tool that can significantly help the user to track the processing history performed on data in jMRUI. The created tool is oriented to be user-friendly, robust and easy to use. The database GUI allows the user to browse the whole processing history of a selected File and learn e.g. what processing lead to the results, where the original data are stored, to obtain the list of all processing actions performed on spectra.

Jana Starcukova - One of the best experts on this subject based on the ideXlab platform.

  • Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance
    BMC Bioinformatics, 2017
    Co-Authors: Michał Jabłoński, Jana Starcukova, Zenon Starčuk
    Abstract:

    BackgroundProton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and File Identification.ResultsA database storing all processing steps, parameters and Files used in processing was developed for jMRUI. The solution was developed in Java, authors used a SQL database for robust storage of parameters and SHA-256 hash code for unambiguous File Identification. The developed system was integrated directly in jMRUI and it will be publically available. A graphical user interface was implemented in order to make the user experience more comfortable. The database operation is invisible from the point of view of the common user, all tracking operations are performed in the background.ConclusionsThe implemented jMRUI database is a tool that can significantly help the user to track the processing history performed on data in jMRUI. The created tool is oriented to be user-friendly, robust and easy to use. The database GUI allows the user to browse the whole processing history of a selected File and learn e.g. what processing lead to the results, where the original data are stored, to obtain the list of all processing actions performed on spectra.

  • Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance.
    BMC Bioinformatics, 2017
    Co-Authors: Michal Jablonski, Jana Starcukova, Zenon Starčuk
    Abstract:

    Background Proton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and File Identification.

Sasha Ames - One of the best experts on this subject based on the ideXlab platform.

  • QMDS: A File System Metadata Management Service Supporting a Graph Data Model-Based Query Language
    2011 IEEE Sixth International Conference on Networking Architecture and Storage, 2011
    Co-Authors: Sasha Ames, Maya B. Gokhale, Carlos Maltzahn
    Abstract:

    File system metadata management has become a bottleneck for many data-intensive applications that rely on high-performance File systems. Part of the bottleneck is due to the limitations of an almost 50 year old interface standard with metadata abstractions that were designed at a time when high-end File systems managed less than 100 MB. Today's high-performance File systems store 7 to 9 orders of magnitude more data, resulting in numbers of data items for which these metadata abstractions are inadequate, such as directory hierarchies unable to handle complex relationships among data. Users of File systems have attempted to work around these inadequacies by moving application-specific metadata management to relational databases to make metadata searchable. Splitting File system metadata management into two separate systems introduces inefficiencies and systems management problems. To address this problem, we propose QMDS: a File system metadata management service that integrates all File system metadata and uses a graph data model with attributes on nodes and edges. Our service uses a query language interface for File Identification and attribute retrieval. We present our metadata management service design and architecture and study its performance using a text analysis benchmark application. Results from our QMDS prototype show the effectiveness of this approach. Compared to the use of a File system and relational database, the QMDS prototype shows superior performance for both ingest and query workloads.

  • NAS - QMDS: A File System Metadata Management Service Supporting a Graph Data Model-Based Query Language
    2011 IEEE Sixth International Conference on Networking Architecture and Storage, 2011
    Co-Authors: Sasha Ames, Maya Gokhale, Carlos Maltzahn
    Abstract:

    File system metadata management has become a bottleneck for many data-intensive applications that rely on high-performance File systems. Part of the bottleneck is due to the limitations of an almost 50 year old interface standard with metadata abstractions that were designed at a time when high-end File systems managed less than 100 MB. Today's high-performance File systems store 7 to 9 orders of magnitude more data, resulting in numbers of data items for which these metadata abstractions are inadequate, such as directory hierarchies unable to handle complex relationships among data. Users of File systems have attempted to work around these inadequacies by moving application-specific metadata management to relational databases to make metadata searchable. Splitting File system metadata management into two separate systems introduces inefficiencies and systems management problems. To address this problem, we propose QMDS: a File system metadata management service that integrates all File system metadata and uses a graph data model with attributes on nodes and edges. Our service uses a query language interface for File Identification and attribute retrieval. We present our metadata management service design and architecture and study its performance using a text analysis benchmark application. Results from our QMDS prototype show the effectiveness of this approach. Compared to the use of a File system and relational database, the QMDS prototype shows superior performance for both ingest and query workloads.