The Experts below are selected from a list of 22374 Experts worldwide ranked by ideXlab platform

Ethan L Miller - One of the best experts on this subject based on the ideXlab platform.

  • spyglass fast scalable Metadata Search for large scale storage systems
    2009
    Co-Authors: Andrew W Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, Ethan L Miller
    Abstract:

    The scale of today's storage systems has made it increasingly difficult to find and manage files. To address this, we have developed Spyglass, a file Metadata Search system that is specially designed for large-scale storage systems. Using an optimized design, guided by an analysis of real-world Metadata traces and a user study, Spyglass allows fast, complex Searches over file Metadata to help users and administrators better understand and manage their files. Spyglass achieves fast, scalable performance through the use of several novel Metadata Search techniques that exploit Metadata Search properties. Flexible index control is provided by an index partitioning mechanism that leverages namespace locality. Signature files are used to significantly reduce a query's Search space, improving performance and scalability. Snapshot-based Metadata collection allows incremental crawling of only modified files. A novel index versioning mechanism provides both fast index updates and "back-in-time" Search of Metadata. An evaluation of our Spyglass prototype using our real-world, large-scale Metadata traces shows Search performance that is 1-4 orders of magnitude faster than existing solutions. The Spyglass index can quickly be updated and typically requires less than 0.1%of disk space. Additionally, Metadata collection is up to 10× faster than existing approaches.

  • high performance Metadata indexing and Search in petascale data storage systems
    2008
    Co-Authors: Andrew W Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, Ethan L Miller
    Abstract:

    Large-scale storage systems used for scientific applications can store petabytes of data and billions of files, making the organization and management of data in these systems a difficult, time-consuming task. The ability to Search file Metadata in a storage system can address this problem by allowing scientists to quickly navigate experiment data and code while allowing storage administrators to gather the information they need to properly manage the system. In this paper, we present Spyglass, a file Metadata Search system that achieves scalability by exploiting storage system properties, providing the scalability that existing file Metadata Search tools lack. In doing so, Spyglass can achieve Search performance up to several thousand times faster than existing database solutions. We show that Spyglass enables important functionality that can aid data management for scientists and storage administrators.

Andrew W Leung - One of the best experts on this subject based on the ideXlab platform.

  • spyglass fast scalable Metadata Search for large scale storage systems
    2009
    Co-Authors: Andrew W Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, Ethan L Miller
    Abstract:

    The scale of today's storage systems has made it increasingly difficult to find and manage files. To address this, we have developed Spyglass, a file Metadata Search system that is specially designed for large-scale storage systems. Using an optimized design, guided by an analysis of real-world Metadata traces and a user study, Spyglass allows fast, complex Searches over file Metadata to help users and administrators better understand and manage their files. Spyglass achieves fast, scalable performance through the use of several novel Metadata Search techniques that exploit Metadata Search properties. Flexible index control is provided by an index partitioning mechanism that leverages namespace locality. Signature files are used to significantly reduce a query's Search space, improving performance and scalability. Snapshot-based Metadata collection allows incremental crawling of only modified files. A novel index versioning mechanism provides both fast index updates and "back-in-time" Search of Metadata. An evaluation of our Spyglass prototype using our real-world, large-scale Metadata traces shows Search performance that is 1-4 orders of magnitude faster than existing solutions. The Spyglass index can quickly be updated and typically requires less than 0.1%of disk space. Additionally, Metadata collection is up to 10× faster than existing approaches.

  • high performance Metadata indexing and Search in petascale data storage systems
    2008
    Co-Authors: Andrew W Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, Ethan L Miller
    Abstract:

    Large-scale storage systems used for scientific applications can store petabytes of data and billions of files, making the organization and management of data in these systems a difficult, time-consuming task. The ability to Search file Metadata in a storage system can address this problem by allowing scientists to quickly navigate experiment data and code while allowing storage administrators to gather the information they need to properly manage the system. In this paper, we present Spyglass, a file Metadata Search system that achieves scalability by exploiting storage system properties, providing the scalability that existing file Metadata Search tools lack. In doing so, Spyglass can achieve Search performance up to several thousand times faster than existing database solutions. We show that Spyglass enables important functionality that can aid data management for scientists and storage administrators.

Harald Reiterer - One of the best experts on this subject based on the ideXlab platform.

  • Improve the Access to Image Data by Combining Content- Based Semantic with Common Metadata Image Retrieval in a Zoomable User Interface
    2010
    Co-Authors: Fredrik Gundelsweiler, Harald Reiterer
    Abstract:

    This paper presents our work on an operational semantic image retrieval prototype. The original image database consists of more than 600.000 images and Metadata sets. Our prototype is working with a subset of 13.421 images which we use for test purposes. The main problems we are confronted with are the amount of data and therefore the location of an item of interest, the different Search strategies that are used, useful interaction techniques and how to combine content-based image retrieval (cbir) with usual Metadata Search to support the users in their tasks. We combine a zoom based interaction concept with a dynamic query Metadata Search and a cbir similarity Search based on GIFT (Gnu Image Finding Tool) [2] to simplify Search and exploration and to solve the stated problems. Our suggestion is that the proposed concept can also be adapted to other data spaces (e.g. videos or documents). Author Keywords Zoomable user interface, content based image retrieval, Metadata retrieval, semantic-based image retrieval. ACM Classification Keywords H5.m. Information interfaces (e.g., HCI): Miscellaneous

  • improve the access to image data by combining content based semantic with common Metadata image retrieval in a zoomable user interface
    2008
    Co-Authors: Fredrik Gundelsweiler, Harald Reiterer
    Abstract:

    This paper presents our work on an operational semantic image retrieval prototype. The original image database consists of more than 600.000 images and Metadata sets. Our prototype is working with a subset of 13.421 images which we use for test purposes. The main problems we are confronted with are the amount of data and therefore the location of an item of interest, the different Search strategies that are used, useful interaction techniques and how to combine content-based image retrieval (cbir) with usual Metadata Search to support the users in their tasks. We combine a zoom based interaction concept with a dynamic query Metadata Search and a cbir similarity Search based on GIFT (Gnu Image Finding Tool) [2] to simplify Search and exploration and to solve the stated problems. Our suggestion is that the proposed concept can also be adapted to other data spaces (e.g. videos or documents).

Shankar Pasupathy - One of the best experts on this subject based on the ideXlab platform.

  • spyglass fast scalable Metadata Search for large scale storage systems
    2009
    Co-Authors: Andrew W Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, Ethan L Miller
    Abstract:

    The scale of today's storage systems has made it increasingly difficult to find and manage files. To address this, we have developed Spyglass, a file Metadata Search system that is specially designed for large-scale storage systems. Using an optimized design, guided by an analysis of real-world Metadata traces and a user study, Spyglass allows fast, complex Searches over file Metadata to help users and administrators better understand and manage their files. Spyglass achieves fast, scalable performance through the use of several novel Metadata Search techniques that exploit Metadata Search properties. Flexible index control is provided by an index partitioning mechanism that leverages namespace locality. Signature files are used to significantly reduce a query's Search space, improving performance and scalability. Snapshot-based Metadata collection allows incremental crawling of only modified files. A novel index versioning mechanism provides both fast index updates and "back-in-time" Search of Metadata. An evaluation of our Spyglass prototype using our real-world, large-scale Metadata traces shows Search performance that is 1-4 orders of magnitude faster than existing solutions. The Spyglass index can quickly be updated and typically requires less than 0.1%of disk space. Additionally, Metadata collection is up to 10× faster than existing approaches.

  • high performance Metadata indexing and Search in petascale data storage systems
    2008
    Co-Authors: Andrew W Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, Ethan L Miller
    Abstract:

    Large-scale storage systems used for scientific applications can store petabytes of data and billions of files, making the organization and management of data in these systems a difficult, time-consuming task. The ability to Search file Metadata in a storage system can address this problem by allowing scientists to quickly navigate experiment data and code while allowing storage administrators to gather the information they need to properly manage the system. In this paper, we present Spyglass, a file Metadata Search system that achieves scalability by exploiting storage system properties, providing the scalability that existing file Metadata Search tools lack. In doing so, Spyglass can achieve Search performance up to several thousand times faster than existing database solutions. We show that Spyglass enables important functionality that can aid data management for scientists and storage administrators.

Timothy Bisson - One of the best experts on this subject based on the ideXlab platform.

  • spyglass fast scalable Metadata Search for large scale storage systems
    2009
    Co-Authors: Andrew W Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, Ethan L Miller
    Abstract:

    The scale of today's storage systems has made it increasingly difficult to find and manage files. To address this, we have developed Spyglass, a file Metadata Search system that is specially designed for large-scale storage systems. Using an optimized design, guided by an analysis of real-world Metadata traces and a user study, Spyglass allows fast, complex Searches over file Metadata to help users and administrators better understand and manage their files. Spyglass achieves fast, scalable performance through the use of several novel Metadata Search techniques that exploit Metadata Search properties. Flexible index control is provided by an index partitioning mechanism that leverages namespace locality. Signature files are used to significantly reduce a query's Search space, improving performance and scalability. Snapshot-based Metadata collection allows incremental crawling of only modified files. A novel index versioning mechanism provides both fast index updates and "back-in-time" Search of Metadata. An evaluation of our Spyglass prototype using our real-world, large-scale Metadata traces shows Search performance that is 1-4 orders of magnitude faster than existing solutions. The Spyglass index can quickly be updated and typically requires less than 0.1%of disk space. Additionally, Metadata collection is up to 10× faster than existing approaches.

  • high performance Metadata indexing and Search in petascale data storage systems
    2008
    Co-Authors: Andrew W Leung, Minglong Shao, Timothy Bisson, Shankar Pasupathy, Ethan L Miller
    Abstract:

    Large-scale storage systems used for scientific applications can store petabytes of data and billions of files, making the organization and management of data in these systems a difficult, time-consuming task. The ability to Search file Metadata in a storage system can address this problem by allowing scientists to quickly navigate experiment data and code while allowing storage administrators to gather the information they need to properly manage the system. In this paper, we present Spyglass, a file Metadata Search system that achieves scalability by exploiting storage system properties, providing the scalability that existing file Metadata Search tools lack. In doing so, Spyglass can achieve Search performance up to several thousand times faster than existing database solutions. We show that Spyglass enables important functionality that can aid data management for scientists and storage administrators.