Showing papers on "Meta Data Services published in 2011"

Metadata Realities for Cyberinfrastructure: Data Authors as Metadata Creators.

Abstract: Author(s): Mayernik, Matthew S | Abstract: As digital data creation technologies become more prevalent, data and metadata management are necessary to make data available, usable, sharable, and storable. Researchers in many scientific settings, however, have little experience or expertise in data and metadata management. In this dissertation, I explore the everyday data and metadata management practices of researchers through a multi-sited ethnographic study of metadata creation by researchers in the Center for Embedded Networked Sensing (CENS). In studying metadata practices, I focused on the ways that researchers document, describe, annotate, organize, and manage their data, both for their own use and the use of researchers outside of their project. This study illustrates how researchers within CENS rarely create documentation that is not directly tied to their own use of their data, and correspondingly, they rarely share data with users from outside of their immediate projects. From these observations, I develop a metadata typology that includes six components, including metadata for: data identity, data characteristics, data quality, data collection equipment, data collection methods, and data analysis methods. I use a framework of accountability to discuss the ways that metadata practices fit within social research settings. Metadata are situated in regimes of mutual accountability in which researchers learn what is important to document, what counts as sufficient documentation, and how documentation practices are to be accounted for in social research settings. Researchers work within social ontologies in which “metadata-for-data sharing” have very low visibility. As a consequence, when asked to create metadata descriptions of the data for a shared CENS metadata registry, researchers lack specific data users, and thus describe their data for members of their most likely “imagined public:” other researchers with shared research interests and methods. I argue that the cyberinfrastructure vision of wide-spread data sharing is fundamentally mis-aligned with the realities of the day-to-day metadata practices of researchers in small-scale field sciences.

Method and system of generating a data lineage repository with lineage visibility, snapshot comparison and version control in a cloud-computing platform

Abstract: In one exemplary embodiment, a computer-implemented method of a database management system including the step of obtaining a metadata about a data from a metadata source. The metadata is converted to an extensible markup language (XML). XML variant or text formatted file. The formatted file is uploaded to a central repository. The formatted file is parsed to acquire information about the data. A data structure that includes the information about the data is generated. The data structure can be stored in a database cluster resident in a cloud computing platform. The metadata source can be an extract, transform and load (ETL) server or a data warehouse server. A dashboard visualization of the data lineage information can be rendered for display with a graphical user interface.

Data sharing and retrieval using OAI-PMH

Abstract: There is a growing consensus for the need to store and archive digital data, particularly for publicly funded research. Long-term preservation of that data generally requires some form of institutional archive, such as those directed to particular scientific communities of practice. Given that data is often of use to multiple communities of practice, which may have differing norms for data and metadata structure and semantics, effective standards for data and metadata exchange are important factors for users to be able to find and retrieve data. Toolsets that provide a coherent presentation of information across multiple standards are important for data search and access. One such toolset, Mercury, is a open-source metadata harvesting, data discovery, and access system, built for researchers to search for, share and obtain spatiotemporal data used across a range of climate and ecological sciences. Mercury is used across multiple projects to provide a coherent search interface for spatiotemporal data described in any of several metadata formats. Mercury has recently been extended to enable harvesting and distribution of metadata using the Open Archive Initiative Protocol for Metadata Handling (OAI-PMH). In this paper we describe Mercury’s capabilities with multiple metadata formats, in general, and, more specifically, the results of our OAI-PMH implementations and the lessons learned.

System and method of organizing and editing metadata

Abstract: In the metadata organization system a metadata classification module dynamically reads metadata organization information, such as, for example, metadata field information, metadata display set information, and metadata category grouping information, from the metadata organization database, reads metadata from a metadata database, and organizes the metadata for display to a user in a graphical user interface. In the graphical user interface, a user may add, delete, and/or modify the metadata in the display. As the user changes the metadata, the metadata database is updated and the user's changes are reflected in the graphical user interface.

Managing Biomedical Image Metadata for Search and Retrieval of Similar Images

Abstract: Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations (“semantic” metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system’s “match observations” function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.

Managing data sets by reasoning over captured metadata

Abstract: A data set manager is configured to interact with data processing elements of an information processing system. The data set manager comprises a metadata capture module configured to access or otherwise obtain metadata characterizing data sets associated with the data processing elements, a reasoning module configured to perform one or more reasoning operations on the metadata, and an action recommendation module configured to identify one or more recommended actions for the data processing elements based at least in part on results of the reasoning operations. The metadata characterizes properties of the data sets and relationships among the data sets, and may be defined in accordance with at least one of a specified ontology and a specified class. The data set manager and associated data processing elements may be implemented, by way of example, in cloud infrastructure of a cloud service provider, or on another type of processing platform.

Managing business objects data sources

Abstract: Methods, non-transitory computer-readable media, and systems for managing business objects data sources. Multiple business objects data sources, each storing multiple data items, are accessed. For each data source, multiple computer-searchable index documents and multiple metadata tables, including master tables and mapping tables, are generated. The multiple computer-searchable index documents and the multiple metadata tables are provided to perform a search for data items in the multiple business objects data sources.

Mind your metadata: exploiting semantics for configuration, adaptation, and provenance in scientific workflows

Abstract: Scientific metadata containing semantic descriptions of scientific data is expensive to capture and is typically not used across entire data analytic processes We present an approach where semantic metadata is generated as scientific data is being prepared, and then subsequently used to configure models and to customize them to the data The metadata captured includes sensor descriptions, data characteristics, data types, and process documentation This metadata is then used in a workflow system to select analytic models dynamically and to set up model parameters automatically In addition, all aspects of data processing are documented, and the system is able to generate extensive provenance records for new data products based on the metadata As a result, the system can dynamically select analytic models based on the metadata properties of the data it is processing, generating more accurate results We show results in analyzing stream metabolism for watershed ecosystem management

Method and system for metadata driven processing of federated data

Abstract: A method includes identifying a first set of metadata associated with a database and retrieving the first set of metadata from one or more first memory locations. The method also includes identifying, based on the first set of metadata, a second set of metadata associated with the database and retrieving the second set of metadata from one or more second memory locations. The method further includes combining the first set of metadata and the second set of metadata into a set of combined metadata.

Metadata-based data access and control

Abstract: Data access is controlled using metadata in the data being stored. As consistent with various example embodiments, data is stored on behalf of one or more of a multitude of disparate users and/or applications using the data, based upon metadata in the data itself and storage rules for the metadata. In one implementation, a rules-based storage and access system retrieves storage rules based upon metadata in received data, and executes the storage rules to select a file system in which to store the received data, from a plurality of file systems having different data retrieval characteristics such as speed, security and quality.

Methods and systems for caching data communications over computer networks

Abstract: A computer-implemented method of caching multi-session data communications in a computer network, includes the steps of: (a) receiving, intercepting, or monitoring one or more data sessions between a client executing a multi-session application for retrieving a desired content object and one or more metadata services, said client communicating with the one or more metadata services to discover metadata for the content object; (b) analyzing queries and responses exchanged between the client and the one of more metadata services to discover metadata for the content object; (c) receiving or intercepting subsequent data sessions between the client and content sources; (d) identifying a data protocol used by the client and identifying data queries within the data sessions; (e) identifying the content object or portions thereof requested by the client in the data queries; and (f) determining if the content object or portions thereof are stored in cache and, if so, sending the content object or portions thereof stored in cache to the client, and, if not, sending the data queries to the content sources, storing data responses from the content sources, and sending the data responses to the client.

Real-time mapping and navigation of multiple media types through a metadata-based infrastructure

Abstract: Methods and systems may involve receiving metadata associated with an activity of a user with respect to a first device, and capturing discovery content from one or more media sources based on the metadata. In addition, the discovery content and the metadata may be presented to the user via a second device.

Metadata for digital collections : a how-to-do-it manual

Abstract: Foreword - Diane I. Hillmann 1. Introduction to Metadata for Digital Collections 2. Introduction to Resource Description and Dublin Core 3. Resource Identification and Responsibility Elements 4. Resource Content and Relationship Elements 5. Controlled Vocabularies for Improved Resource Discovery 6. XML-Encoded Metadata 7. MODS:The Metadata Object Description Schema 8. VRA Core: The Visual Resources Association Core Categories 9. Metadata Interoperability, Shareability, and Quality 10. Designing and Documenting a Metadata Scheme 11. Metadata, Linked Data, and the Semantic Web.

Method and system of hierarchical metadata management and application

Abstract: A method of metadata management is provided. Metadata associated with information objects is important in the management and classification of electronic information referred to as information objects. A metadata schema structure definition is defined enabling a metadata schema to be applied to the information objects to ensure consistent creation and management of hierarchical metadata across and organization and interoperability between different metadata schemas.

Towards an e-Infrastructure for urban research across Australia

Abstract: Australian urban and built environment research covers a multitude of research disciplines investigating social, economic and physical phenomena at a multitude of spatial and temporal scales and across diverse aggregation levels, from individual-level through to cohorts and populations, and across a range of scenarios, e.g. public health, voting patterns. The development of a common software platform (e-Infrastructure) to meet the needs of such research communities involves tackling many challenges associated with data intensive areas of research. This includes dealing with data sets from a multitude of federal, state, municipal, academic and private institutions, which hold vast arrays of heterogeneous data. For many researchers these data sets are difficult to discover, access, interrogate and use more generally. It is also unrealistic to expect researchers to always have the technical capability and capacity to handle such large amounts of data, or to develop data processing tools making use of such data sets, or be able to run computationally intensive simulations and models based on these data sets. Islands of expertise and islands (silos) of data currently exist that have fragmented urban research and thwarted a holistic approach to the study of the Australian urban and built environment system. The Australian Urban Research Infrastructure Network (AURIN - www.aurin.org.au) is a $20m SuperScience initiative established across Australia that seeks to address this directly by creating an e-Infrastructure aiming at bridging these gaps, by allowing researchers to conduct collaborative research in a security-enabled, browser-based environment providing seamless and transparent access to the distributed data and computational resources across Australia. These include metadata services, federated datasets, data integration and interrogation services, together with advanced visualization, collaboration and data storage capabilities. The goal is to provide access to rich datasets, state-of-the-art data processing tools, as well as a knowledge base where good research practices can be followed and used to assist researchers when navigating through vast data holdings to couple appropriate data and analytical tools for a range of urban research endeavors. In this paper, we address the fundamental question behind the establishment of this infrastructure, i.e. how to design a versatile and flexible software platform (e-Infrastructure) for urban research? The approach described is centered on establishment of a range of demonstrator projects addressing specific urban and built environment themes and the challenges they give rise to through a common e-Infrastructure. We believe that such an approach will allow delivery of early functionality in supporting a range of urban research scenarios, and at the same time support novel links between tools traditionally not applied beyond individual research fields. Through a common (core) e-Infrastructure, we expect to develop urban research capability that will offer a step change in how urban research is currently conducted, to support multi- and inter-disciplinary research scenarios whilst preserving full functionality in individual urban research strands. We describe the initial design stages of the AURIN e-Infrastructure from a technical perspective. The approach chosen is based on past experiences from a variety of eResearch initiatives, such as the UK e-Science National e-Infrastructure for Social Simulation (NeISS - www.neiss.org.au) project (Birkin et al., 2010); the Data Management through e-Social Science (DAMES - www.dames.org.au) project (McCafferty et al., 2009); the Spatial Information Software Stack (SISS) eResearch Facility (Liao et al., 2009), and the workflow-based image annotation using geographic information retrieval of TRIPOD (Purves et al., 2010). We illustrate the utility of the approach taken based on an initial set of demonstration projects exploring demographic and economic investigations of the Australian urban system.

The Large Scale Data Facility: Data Intensive Computing for Scientific Experiments

Abstract: The Large Scale Data Facility (LSDF) at the Karlsruhe Institute of Technology was started end of 2009 with the aim of supporting the growing requirements of data intensive experiments. In close cooperation with the involved scientific communities, the LSDF provides them not only with adequate storage space but with a directly attached analysis farm and -- more importantly -- with value added services for their big scientific data-sets. Analysis workflows are supported through the mixed Hadoop and Open Nebula Cloud environments directly attached to the storage, and enable the efficient processing of the experimental data. Metadata handling is a central part of the LSDF, where a metadata repository, community specific metadata schemes, graphical tools, and APIs were developed for accessing and efficiently organizing the stored data-sets.

An Analysis of Problems in Metadata Records

Abstract: Metadata plays an important role in digital libraries. But to be useful, metadata records must be problem free. When problems are present in the metadata, resources are not correctly represented and users are not able to reap the benefits of metadata. To minimize, if not eliminate, such problems, it is essential to understand the kinds of problems that can occur in metadata records. In this paper, problems found in metadata records as reported in the literature are compared and analyzed. It is found that five categories of metadata problems can be identified. These are the problems of Incorrect Values, Incorrect Elements, Missing Information, Information Loss, and Inconsistent Value Representation. Given that these problems are detrimental to the services that can be provided by metadata, preventive or corrective measures need to be put in place so as to ensure that the benefits derived from using metadata balance the costs and efforts spent in the creation of metadata records.

An unsupervised heuristic-based approach for bibliographic metadata deduplication

Abstract: Digital libraries of scientific articles contain collections of digital objects that are usually described by bibliographic metadata records. These records can be acquired from different sources and be represented using several metadata standards. These metadata standards may be heterogeneous in both, content and structure. All of this implies that many records may be duplicated in the repository, thus affecting the quality of services, such as searching and browsing. In this article we present an approach that identifies duplicated bibliographic metadata records in an efficient and effective way. We propose similarity functions especially designed for the digital library domain and experimentally evaluate them. Our results show that the proposed functions improve the quality of metadata deduplication up to 188% compared to four different baselines. We also show that our approach achieves statistical equivalent results when compared to a state-of-the-art method for replica identification based on genetic programming, without the burden and cost of any training process.

Metadata quality and interoperability of GLAM digital images

Abstract: Purpose – This study aims to explore how metadata have been applied in GLAM (galleries, libraries, archives and museums) institutions in New Zealand (NZ) and to analyse its overall quality with the interoperability of the metadata element set especially in mind.Design/methodology/approach – The first stage of data collection involved an analysis of the metadata records from 16 institutions from the NZ GLAM sector to examine the types and extent of metadata used. However, by looking at publicly accessible metadata records, it was impossible to determine the full extent of metadata created, especially when there could be metadata that were kept in‐house. This was complemented with interviewing of staff from the institutions concerned.Findings – The study found that metadata records for digital images in four types of institutions have different emphases on metadata functions and a variety of metadata are not applied on a consistent basis. The lack of technical data in metadata records means that digital vis...

Metadata driven customization of a software-implemented business process

Abstract: A method of facilitating customization of a software-implemented business process includes storing, within a mobile computing device, a subscription list of entities. The subscription list being defined by subscription metadata. Customized data is received. The customized data corresponds to the entities identified in the subscription list. The received customized metadata is stored on the mobile computing device.

Data context selection in business analytics reports

Abstract: A method for changing data context in a business analytics report is described herein. In one embodiment, such a method includes providing a business analytics report, wherein the report contains data and current metadata categories describing the data. The method enables selection of a current metadata category directly on the business analytics report. The method displays a metadata hierarchy associated with the current metadata category. This metadata hierarchy may include the current metadata category as well as related metadata categories arranged in a tree-like structure. The method further enables selection of a related metadata category from the metadata hierarchy to apply to the business analytics report. The method automatically updates the business analytics report with the metadata category and populates the report with the associated data. A corresponding computer program product and apparatus are also disclosed.

Bulk access to metadata in a service-oriented business framework

Abstract: A bulk service interface can be instantiated for each of at least one service provider providing a plurality of business objects using a stored definition of a plurality of metadata properties relating to generation of a user interface that incorporates content of a plurality of business objects. The bulk service interface can query the stored definition and access, in a bulk metadata access process, dynamic metadata required for instantiation of the plurality of business objects. If the stored definition has not previously been received, a default bulk services access process that queries an existing dynamic metadata access interface of each of the plurality of business objects can be initiated to retrieve the dynamic metadata from each of the plurality of business objects. An instance of the user interface can be generated using dynamic metadata accessed by the bulk service interface and/or the retrieved by the default bulk services access process.

Metadata driven collaboration between applications and web services

Abstract: Methods and systems may involve identifying metadata associated with a local application, and using an operating system-independent chat protocol to pass the metadata to a remote application. The metadata may include information to be presented to a user of a device executing the remote application, information to be used to locate more information for presentation to the user of the other device, information to be used to coordinate operation of multiple devices, and so forth. In one example, the metadata is passed using a protocol such as XMPP (Extensible Messaging and Presence Protocol).

Static semantic analysis of dynamic languages

Abstract: Analyzing dynamic source code. A method includes accessing a specific metadata format data structure. The data structure was created by obtaining one or more first data structures defining constructs in a body of dynamic language source code. From the one or more first data structures, identifier information is extracted for one or more of the defined constructs. Knowledge about the constructs is augmented. The metadata format data structure is parsed to compute metrics about the metadata format data structure. The metrics about the metadata format data structure are provided to a user.

Hierarchical Classification of OAI Metadata Using the DDC Taxonomy

Abstract: In the area of digital library services, the access to subject-specific metadata of scholarly publications is of utmost interest. One of the most prevalent approaches for metadata exchange is the XML-based Open Archive Initiative (OAI) Protocol for Metadata Harvesting (OAI-PMH). However, due to its loose requirements regarding metadata content there is no strict standard for consistent subject indexing specified, which is furthermore needed in the digital library domain. This contribution addresses the problem of automatic enhancement of OAI metadata by means of the most widely used universal classification schemes in libraries—the Dewey Decimal Classification (DDC). To be more specific, we automatically classify scientific documents according to the DDC taxonomy within three levels using a machine learning-based classifier that relies solely on OAI metadata records as the document representation. The results show an asymmetric distribution of documents across the hierarchical structure of the DDC taxonomy and issues of data sparseness. However, the performance of the classifier shows promising results on all three levels of the DDC.

Delay-Tolerant Networking Metadata Extension Block

Abstract: This document defines an extension block that may be used with the DTN Bundle Protocol. This Metadata Extension Block is designed to carry additional information that DTN nodes can use to make processing decisions regarding bundles, such as deciding whether to store a bundle or determining to which nodes to forward a bundle. The metadata that is carried in a metadata block must be formatted according to the metadata type that is identified in the block's metadata type field. One specific metadata type, for carrying URIs as metadata, is defined in this document. Other metadata types may be defined in separate documents. This document is a product of the Delay Tolerant Networking Research Group and has been reviewed by that group. No objections to its publication as an RFC were raised.

Search tool that utilizes scientific metadata matched against user-entered parameters

Abstract: A method for providing proximate dataset recommendations can begin with the creation of metadata records corresponding to datasets that represent scientific data by a scientific dataset search tool. The metadata records can conform to a standardized structural definition, and may be hierarchical. Values for the data elements of the metadata records can be contained within the datasets. Metadata records with a value that is proximate to a user-entered search parameter can be identified. A proximity score can be calculated for each identified metadata record. The proximity score can express a relevance of the corresponding dataset to the user-entered search parameters. The identified metadata records can be arranged in descending order by the calculated proximity rating, creating a list of proximate dataset results. The proximate dataset results can be presented within a user interface.

A study of metadata standards

Abstract: Purpose – The purpose of this paper is to investigate the metadata standards available worldwide and to study those in detail. The paper presents the detailed analysis of these standards.Design/methodology/approach – The methodology adopted was investigative. Data were collected through e‐mails and visiting the relevant web sites.Findings – No one existing metadata standard covers all the required facets. In such cases, an implementer is require to create a new scheme with the desired metadata elements but such schemes create a number of issues in interoperating. Another solution to this problem is to create extra elements to fill gaps in the coverage. Interoperability problems can be overcome by publications of new elements declaring their definitions, formats and so on.Originality/value – Few studies have compared some of the well‐known standards. This study gives a detailed analysis of the metadata standards available worldwide and it will help in comparing and adopting the required standard for the re...

What Is Metadata

Abstract: The one-phrase definition of “metadata” – “data about data” – is too broad to have practical value. However, the uses of metadata itself across all of computing, and the ways in which it is represented, are so numerous and diverse that it would be hard to find two practitioners who think about it in exactly the same terms. An inspection of Wikipedia’s article on the subject,1 especially the accompanying discussion, will make it clear that the topic has grown to the point where its editors believe that the article has become too large and unwieldy – and yet it barely touches on the themes that are of specific relevance to this book.