Showing papers on "Meta Data Services published in 2014"

openPDS: Protecting the Privacy of Metadata through SafeAnswers

Abstract: The rise of smartphones and web services made possible the large-scale collection of personal metadata. Information about individuals' location, phone call logs, or web-searches, is collected and used intensively by organizations and big data researchers. Metadata has however yet to realize its full potential. Privacy and legal concerns, as well as the lack of technical solutions for personal metadata management is preventing metadata from being shared and reconciled under the control of the individual. This lack of access and control is furthermore fueling growing concerns, as it prevents individuals from understanding and managing the risks associated with the collection and use of their data. Our contribution is two-fold: (1) we describe openPDS, a personal metadata management framework that allows individuals to collect, store, and give fine-grained access to their metadata to third parties. It has been implemented in two field studies; (2) we introduce and analyze SafeAnswers, a new and practical way of protecting the privacy of metadata at an individual level. SafeAnswers turns a hard anonymization problem into a more tractable security one. It allows services to ask questions whose answers are calculated against the metadata instead of trying to anonymize individuals' metadata. The dimensionality of the data shared with the services is reduced from high-dimensional metadata to low-dimensional answers that are less likely to be re-identifiable and to contain sensitive information. These answers can then be directly shared individually or in aggregate. openPDS and SafeAnswers provide a new way of dynamically protecting personal metadata, thereby supporting the creation of smart data-driven services and data science research.

Towards next generation BI systems : the analytical metadata challenge

Abstract: Next generation Business Intelligence (BI) systems require integration of heterogeneous data sources and a strong user-centric orientation. Both needs entail machine-processable metadata to enable automation and allow end users to gain access to relevant data for their decision making processes. Although evidently needed, there is no clear picture about the necessary metadata artifacts, especially considering user support requirements. Therefore, we propose a comprehensive metadata framework to support the user assistance activities and their automation in the context of next generation BI systems. This framework is based on the findings of a survey of current user-centric approaches mainly focusing on query recommendation assistance. Finally, we discuss the benefits of the framework and present the plans for future work.

Linked Data for Libraries, Archives and Museums: How to Clean, Link and Publish Your Metadata

Abstract: Libraries, archives and museums are facing up to the challenge of providing access to fast growing collections whilst managing cuts to budgets Key to this is the creation, linking and publishing of good quality metadata as Linked Data that will allow their collections to be discovered, accessed and disseminated in a sustainable manner Metadata experts Seth van Hooland and Ruben Verborgh introduce the key concepts of metadata standards and Linked Data and how they can be practically applied to existing metadata, giving readers the tools and understanding to achieve maximum results with limited resources Readers will learn how to critically assess and use (semi-)automated methods of managing metadata through hands-on exercises within the book and on the accompanying website Each chapter is built around a case study from institutions around the world, demonstrating how freely available tools are being successfully used in different metadata contexts This handbook delivers the necessary conceptual and practical understanding to empower practitioners to make the right decisions when making their organizations resources accessible on the Web

Metadata quality in learning object repositories: a case study

Abstract: Purpose – This paper aims to address the issue of poor quality of metadata records describing educational content in Learning Object Repositories (LORs) Through this, it aims to improve the discoverability of learning objects in such LORs through a structured process that supports metadata creation Design/methodology/approach – This paper presents a proposed metadata quality assessment certification process for LORs The process was designed as a generic approach that may be customized to fit various application domains Findings – Initial results from the application of the process in the context of a specific LOR report an improvement of the quality of about 11,000 metadata records More specifically, metadata completeness for all metadata elements used in the repository under examination was significantly improved from 30 percent to 85 percent Research limitations/implications – The main limitation of the findings is that they come from the application of the proposed process on a relatively small r

Configurable metadata-based automation and content classification architecture for cloud-based collaboration platforms

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

DataID: towards semantically rich metadata for complex datasets

Abstract: The constantly growing amount of Linked Open Data (LOD) datasets constitutes the need for rich metadata descriptions, enabling users to discover, understand and process the available data. This metadata is often created, maintained and stored in diverse data repositories featuring disparate data models that are often unable to provide the metadata necessary to automatically process the datasets described. This paper proposes DataID, a best-practice for LOD dataset descriptions which utilize RDF files hosted together with the datasets, under the same domain. We are describing the data model, which is based on the widely used DCAT and VoID vocabularies, as well as supporting tools to create and publish DataIDs and use cases that show the benefits of providing semantically rich metadata for complex datasets. As a proof of concept, we generated a DataID for the DBpedia dataset, which we will present in the paper.

Metadata-driven workflows and integration with genomic data processing systems and techniques

Abstract: Systems, methods and computer program products configured to provide and perform metadata-based workflow management are disclosed. The inventive subject matter includes a computer readable storage medium having computer readable program instructions embodied therewith. The computer readable program instructions are configured to: initiate a workflow configured to process data; associate the data with metadata; and drive at least a portion of the workflow based on at least some of the metadata. The metadata include anchoring metadata; common metadata; and custom metadata. Inventive subject matter also encompasses a method for managing genomic data processing workflows using metadata includes: initiating a workflow; receiving a request to manage the workflow using metadata comprising: anchoring metadata, common metadata, and custom metadata, associating the metadata with the data; and driving at least a portion of the workflow based on the metadata. The workflow involves genomic analyses.

Metadata for Big Data: A preliminary investigation of metadata quality issues in research data repositories

Abstract: Data-driven approaches to scientific research have generated new types of repositories that provide scientists the means necessary to store, share and re-use big data-sets generated at various stages of the research process. As the number and heterogeneity of research data repositories increase, it becomes critical for scientists to solve data quality problems associated to the data-sets stored in these repositories. To date, several authors have been focused on the data quality issues associated to the data-sets stored in the repositories, yet there is little knowledge about the quality problems of the metadata used to describe these data-sets. Metadata is important for the long-term sustainability of research data repositories and data re-use. The aim of the research reported in this paper was to identify the data quality problems associated with the metadata used in the Dryad data repository. The paper concludes with some recommendations for improving the quality of metadata in research data repositories.

Metadata quality issues in learning repositories

Abstract: Metadata lies at the heart of every digital repository project in the sense that it defines and drives the description of digital content stored in the repositories. Metadata allows content to be successfully stored, managed and retrieved but also preserved in the long-term. Despite the enormous importance of metadata in digital repositories, one that is widely recognized, studies indicate that what is defined as metadata quality, is relatively low in most cases of digital repositories. Metadata quality is loosely defined as "fitness for purpose" meaning that low quality of metadata means that metadata cannot fulfill its purpose which is to allow for the successful storage, management and retrieval of resources. In practice, low metadata quality leads to ineffective searches for content, ones that recall the wrong resources or even worse, no resources which makes them invisible to the intended user, that is the "client" of each digital repository. The present dissertation approaches this problem by proposing a comprehensive metadata quality assurance method, namely the Metadata Quality Assurance Certification Process (MQACP). The basic idea of this dissertation is to propose a set of methods that can be deployed throughout the lifecycle of a repository to ensure that metadata generated from content providers are of high quality. These methods have to be straightforward, simple to apply with measurable results. They also have to be adaptable with minimum effort so that they can be used in different contexts easily. This set of methods was described analytically, taking into account the actors needed to apply them, describing the tools needed and defining the anticipated outcomes. In order to test our proposal, we applied it on a Learning Federation of repositories, from day 1 of its existence until it reached its maturity and regular operation. We supported the metadata creation process throughout the different phases of the repositories involved by setting up specific experiments using the methods and tools of the MQACP. Throughout each phase, we measured the resulting metadata quality to certify that the anticipated improvement in metadata quality actually took place. Lastly, through these different phases, the cost of the MQACP application was measured to provide a comparison basis for future applications. Based on the success of this first application, we decided to validate the MQACP approach by applying it on another two cases of a Cultural and a Research Federation of repositories. This would allow us to prove the transferability of the approach to other cases the present some similarities with the initial one but mainly significant differences. The results showed that the MQACP was successfully adapted to the new contexts, with minimum adaptations needed, with similar results produced and also with comparable costs. In addition, looking closer at the common experiments carried out in each phase of each use case, we were able to identify interesting patterns in the behavior of content providers that can be further researched. The dissertation is completed with a set of future research directions that came out of the cases examined. These research directions can be explored in order to support the next version of the MQACP in terms of the methods deployed, the tools used to assess metadata quality as well as the cost analysis of the MQACP methods.

"Big Metadata": The Need for Principled Metadata Management in Big Data Ecosystems

Abstract: Current big data ecosystems lack a principled approach to metadata management. This impedes large organizations' ability to share data and data preparation and analysis code, to integrate data, and to ensure that analytic code makes compatible assumptions with the data it uses. This use-case paper describes the challenges and an in-progress effort to address them. We present a real application example, discuss requirements for "big metadata" drawn from that example as well as other U.S. government analytic applications, and briefly describe an effort to adapt an existing open source metadata manager to support the needs of big data ecosystems.

Metadata quality in digital repositories: Empirical results from the cross-domain transfer of a quality assurance process

Abstract: Metadata quality presents a challenge faced by many digital repositories. There is a variety of proposed quality assurance frameworks applied in repositories that are deployed in various contexts. Although studies report that there is an improvement of the quality of the metadata in many of the applications, the transfer of a successful approach from one application context to another has not been studied to a satisfactory extent. This article presents the empirical results of the application of a metadata quality assurance process that has been developed and successfully applied in an educational context (learning repositories) to 2 different application contexts to compare results with the previous application and assess its generalizability. More specifically, it reports results from the adaptation and application of this process in a library context (institutional repositories) and in a cultural context (digital cultural repositories). Initial empirical findings indicate that content providers seem to be gaining a better understanding of metadata when the proposed process is put in place and that the quality of the produced metadata records increases.

Deploying an application in a cloud computing environment

Abstract: A technique for deploying an application in a cloud computing environment includes: collecting, when a user is deploying an application, metadata and instructions on deploying the application, the metadata comprising service metadata, application metadata and topology metadata, wherein the service metadata comprise metadata on a service required for deploying the application, the application metadata comprise metadata on the application, and the topology metadata comprise metadata indicative of a relationship between the service and the application; and storing the collected metadata and instructions as a model for re-deploying the application.

Using property graphs for rich metadata management in HPC systems

Abstract: HPC platforms are capable of generating huge amounts of metadata about different entities including jobs, users, and files. Simple metadata, which describe the attributes of these entities (e.g., file size, name, and permissions mode), has been well recorded and used in current systems. However, only a limited amount of rich metadata, which records not only the attributes of entities but also relationships between them, are captured in current HPC systems. Rich metadata may include information from many sources, including users and applications, and must be integrated into a unified framework. Collecting, integrating, processing, and querying such a large volume of metadata pose considerable challenges for HPC systems. In this paper, we propose a rich metadata management approach that unifies metadata into one generic property graph. We argue that this approach supports not only simple metadata operations such as directory traversal and permission validation but also rich metadata operations such as provenance query and security auditing. The property graph approach provides an extensible method to store diverse metadata and presents an opportunity to leverage rapidly evolving graph storage and processing techniques.

Metadata extraction and correction for large-scale traffic surveillance videos

Abstract: Metadata is widely used to facilitate user defined queries and high-level event recognition applications in traffic surveillance videos. Current metadata extraction approaches rely on some computer vision algorithms, which are not accurate enough in the real world traffic scenes, and do not deal with big surveillance data efficiently. In this paper, we design a novel metadata extraction and metadata correction system. Firstly, we define the structure of metadata to determine which attribute (e.g., vehicle enter time, license plate number, vehicle type) we need to extract. Based on this structure, we employ a three-phase method to extract metadata. Secondly, we propose a graph-based metadata correction approach for compensating the accuracy of metadata extraction method. It fuses the big metadata of whole camera network, automatically detects suspicious metadata and corrects them based on the metadata spatial-temporal relationship and the image similarity. As the centralized framework may not be able to cope with the huge amount of data generated by traffic surveillance system, our system is implemented in a distributed fashion using Hadoop and HBase. Finally, the experimental results on real world traffic surveillance videos demonstrate the efficiency of our system, and also demonstrate that the metadata quality is significantly improved after metadata correction.

System and method to store third-party metadata in a cloud storage system

Abstract: A cloud storage system identifies and creates metadata associated with a stored file. On receiving a request to access the file with an application, the cloud storage system generates a metadata category associated only with the application, and creates metadata associated with the generated metadata category and corresponding to the file. On receiving a request to access file metadata, the cloud storage system identifies an application associated with the metadata access request, identifies a set of metadata categories associated with the identified application, filters metadata associated with the file based on the identified set of metadata categories, and provides the filtered metadata to the application.

From research to practice: experiences engineering a production metadata database for a scale out file system

Abstract: HP's Store All with Express Query is a scalable commercial file archiving product that offers sophisticated file metadata management and search capabilities [3]. A new REST API enables fast, efficient searching to find all files that meet a given set of metadata criteria and the ability to tag files with custom metadata fields. The product brings together two significant systems: a scale out file system and a metadata database based on LazyBase [10]. In designing and building the combined product, we identified several real-world issues in using a pipelined database system in a distributed environment, and overcame several interesting design challenges that were not contemplated by the original research prototype. This paper highlights our experiences.

Web metadata extraction and semantic indexing for learning objects extraction

Abstract: Secondary-school teachers are in constant need of finding relevant digital resources to support specific didactic goals. Unfortunately, generic search engines do not allow them to identify learning objects among semi-structured candidate educational resources, much less retrieve them by teaching goals. This article describes a multi-strategy approach for semantically guided extraction, indexing and search of educational metadata; it combines machine learning, concept analysis, and corpus-based natural language processing techniques. The overall model was validated by comparing extracted metadata against standard search methods and heuristic-based techniques for Classification Accuracy and Metadata Quality (as evaluated by actual teachers), yielding promising results and showing that this semantically guided metadata extraction can effectively enhance access and use of educational digital material.

Creating context for the experiment record: user-defined metadata

Abstract: The drive towards more transparency in research and open data increases the importance of being able to find information and make links to the data. Metadata is an essential ingredient for facilitating discovery and is used in Electronic Laboratory Notebooks to curate experiment data and associated entries with descriptive information and classification labels that can be used for aggregation and identification. Machine-generated metadata helps with facilitating metadata exchange and enabling interoperability, but such metadata is not necessarily in a form friendly for the humans that also need it. A survey of metadata usage in an ELN developed at the University of Southampton indicates many users do not use metadata effectively. Whilst some groups are comfortable with metadata and are able to design a metadata structure that works effectively, many users have no knowledge of where to start to define metadata or even an understanding of what it is and why it is useful. The metadata used within the notebooks is dominated by a few categories, in particular materials, data formats, and instruments. Further investigation is under way to determine whether this pattern of metadata use is common in other online environments, whether users are more likely to create certain types of metadata, and whether lessons can be learned from other environments to encourage metadata use. These findings will contribute to strategies for encouraging and improving metadata use in ELNs such as improved interface designs, user education, standard schema designs, and encouraging collaboration between same-discipline groups to promote consistency and best practices.

Metadata for research data: current practices and trends

Abstract: This paper reports a study that examined the metadata standards and formats used by a select number of research data services, namely Datacite, Dataverse Network, Dryad, and FigShare. These services make use of a broad range of metadata practices and elements. The specific objective of the study was to investigate the number and nature of metadata elements, metadata elements specific to research data, compliance with interoperability and preservation standards, the use of controlled vocabularies for subject description and access and the extent of support for unique identifiers as well as the common and different metadata elements across these services. The study found that there was a variety of metadata elements used by the research data services and that the use of controlled vocabularies was common across the services. It was found that preservation and unique identifiers are central components of the studied services. An interesting observation was the extent of research data specific metadata elements, with Dryad making use of a wider range of metadata elements specific to research data than other services.

Descriptive Metadata for Scientific Data Repositories: A Comparison of Information Scientist and Scientist Organizing Behaviors

Abstract: The integration of scientific data sets into repository collections is a continuing challenge for research institutions. This study used a concurrent triangulation mixed-methods approach to gather basic quantitative and qualitative data about how 11 information professionals and 16 scientists organized data sets for personal and repository use. The results incorporate comments from study participants that elaborate on descriptive metadata use, data curation practices, and organization styles/preferences.

A theory of digital library metadata : the emergence of enriching and filtering

Abstract: The ever increasing volume and diversity of information objects, technological advances and rising user expectations is causing libraries to face challenges in adequately describing information objects so as to improve the findability and discoverability of these objects by potential end users. Taking these present metadata challenges into account, this thesis inductively explores and develops overarching concepts and principles that are pertinent within both current standards-based and emerging metadata approaches. Adopting a Constructivist Grounded Theory Method, this thesis conducted in-depth interviews with 57 purposefully selected participants, comprised of practising librarians, researchers, metadata consultants and library users. The interview data was analysed using three stages of iterative data analysis: open coding, focused coding and theoretical coding. The analysis resulted in the emergence of four Core Categories, namely, metadata Enriching, Linking, Openness and Filtering. Further integration of the Core Categories resulted in the emergence of a theory of digital library metadata; The Theory of Metadata Enriching and Filtering. The theory stipulates that metadata that has been enriched, by melding standards-based (a priori) and socially-constructed (post-hoc) metadata, cannot be optimally utilised unless the resulting metadata is contextually and semantically linked to both internal and external information sources. Moreover, in order to exploit the full benefits of such linking, metadata must be made openly accessible, where it can be shared, re-used, mixed and matched, thus reducing metadata duplication. Ultimately, metadata that has been enriched (by linking and being made openly accessible) should be filtered for each user, via a flexible, personalised, and re-configurable interface. The theory provides a holistic framework demonstrating the interdependence between expert curated and socially-constructed metadata, wherein the former helps to structure the latter, whilst the latter provides diversity to the former. This theory also suggests a conceptual shift from the current metadata principle of sufficiency and necessity, which has resulted in metadata simplicity, to the principle of metadata enriching where information objects are described using a multiplicity of users’ perspectives (interpretations). Central to this theory is the consideration of users as pro-active metadata creators rather than mere consumers, whilst librarians are creators of a priori metadata and experts at providing structure, granularity, and interoperability to post-hoc metadata. The theory elegantly delineates metadata functions into two: enriching (metadata content) and filtering (interface). By providing underlying principles, this theory should enable standards-agencies, librarians, and systems developers to better address the changing needs of users as well as to adapt themselves to recent technological advances.

Metadata capital: Raising awareness, exploring a new concept

Abstract: EDITOR'S SUMMARY While the value of information is widely recognized, the next step is recognizing metadata as an economic asset. Generating metadata involves costs in technological and human resources, but failure to generate and use metadata can lead to lost opportunity costs. Metadata activities are ultimately motivated by a drive for return on investment. The view of metadata as capital emphasizes that it has value that can rise with reuse. Metadata capital is defined as an asset that captures contextual knowledge about any information object, is produced by human labor or automated processes and yields a product or facilitates some service and can benefit the public. Expediting reuse of data and metadata is the key to maximizing their value, and early research demonstrates value in the linked open vocabulary environment and in reusing URIs. A collaborative project of the National Consortium for Data Science, the Metadata Capital Initiative, is focusing on the value of metadata through reuse in a big data setting to document its specific contributions to technology methods and intellectual advances.

Metadata integration for an archaeology collection architecture

Abstract: During the lifecycle of a research project, from the collection of raw data through study to publication, researchers remain active curators and decide how to present their data for future access and reuse. Thus, current trends in data collections are moving toward infrastructure services that are centralized, flexible, and involve diverse technologies across which multiple researchers work simultaneously and in parallel. In this context, metadata is key to ensuring that data and results remain organized and that their authenticity and integrity are preserved. Building and maintaining it can be cumbersome, however, especially in the case of large and complex datasets. This paper presents our work to develop a collection architecture, with metadata at its core, for a large and varied archaeological collection. We use metadata, mapped to Dublin Core, to tie the pieces of this architecture together and to manage data objects as they move through the research lifecycle over time and across technologies and changing methods. This metadata, extracted automatically where possible, also fulfills a fundamental preservation role in case any part of the architecture should fail.

Toward an emerging principle of linking socially-constructed metadata

Abstract: Current discussions on the use of Linked Data for digital libraries favor standards-based (expert-created) metadata approaches. The inclusion of socially-constructed (user-created) metadata such as tagging, ratings, reviews, and recommendations is scant or absent. While efforts by the world's major national and academic libraries to release their bibliographic data (metadata) are an important step toward the adoption of Linked Data principles, as this article argues, without the metadata richness that would be obtained by including socially-constructed metadata (Web 2.0), the current efforts may result in sub-optimal utilization of the potential of Linked Data. With the aim of exploring and investigating the potential roles of metadata linking, this article presents the results of 21 in-depth interviews conducted with Library and Information Science (LIS) professionals. A constructivist grounded theory analysis of the interview data resulted in the emergence of four metadata principles—namely, the princip...

PROVIDING FILE METADATA QUERIES FOR FILE SYSTEMS USING RESTful APIs

Abstract: Example embodiments relate to providing file metadata queries for file systems using representational state transfer compliant (RESTful) application programming interfaces. In example embodiments, a representational state transfer (REST) request that includes requested attributes and search parameters is received. Then, a metadata source including source attributes that correspond to the requested attributes is identified using the translation configuration. The translation configuration of the metadata source is also used to convert the search parameters to obtain converted parameters that are compatible with the metadata source. At this stage, a metadata query for the metadata source that includes the requested attributes and the converted parameters is created.

SM4AM: A Semantic Metamodel for Analytical Metadata

Abstract: Next generation BI systems emerge as platforms where traditional BI tools meet semi-structured and unstructured data coming from the Web. In these settings, the user-centric orientation represents a key characteristic for the acceptance and wide usage by numerous and diverse end users in their data analysis tasks. System and user related metadata are the base for enabling user assistance features. However, current approaches typically store these metadata in ad-hoc manners. In this paper, we propose a generic and extensible approach for the definition and modeling of the relevant metadata artifacts. We present SM4AM, a Semantic Metamodel for Analytical Metadata created as an RDF formalization of the Analytical Metadata artifacts needed for user assistance exploitation purposes in next generation BI systems. We consider the Linked Data initiative and its relevance for user assistance functionalities. We discuss the metamodel benefits and present directions for future work.

Creating context for the experiment record. User-defined metadata: investigations into metadata usage in the LabTrove ELN.

Abstract: The drive toward more transparency in research, the growing willingness to make data openly available, and the reuse of data to maximize the return on research investment all increase the importance of being able to find information and make links to the underlying data. The use of metadata in Electronic Laboratory Notebooks (ELNs) to curate experiment data is an essential ingredient for facilitating discovery. The University of Southampton has developed a Web browser-based ELN that enables users to add their own metadata to notebook entries. A survey of these notebooks was completed to assess user behavior and patterns of metadata usage within ELNs, while user perceptions and expectations were gathered through interviews and user-testing activities within the community. The findings indicate that while some groups are comfortable with metadata and are able to design a metadata structure that works effectively, many users are making little attempts to use it, thereby endangering their ability to recover data in the future. A survey of patterns of metadata use in these notebooks, together with feedback from the user community, indicated that while a few groups are comfortable with metadata and are able to design a metadata structure that works effectively, many users adopt a "minimum required" approach to metadata. To investigate whether the patterns of metadata use in LabTrove were unusual, a series of surveys were undertaken to investigate metadata usage in a variety of platforms supporting user-defined metadata. These surveys also provided the opportunity to investigate whether interface designs in these other environments might inform strategies for encouraging metadata creation and more effective use of metadata in LabTrove.

System, apparatus and method for enhancing metadata registration workflow

Abstract: Systems, apparatuses and methods are provided to enhance metadata registration workflow, such as in a multi-functional apparatus. User selection of metadata items and entry of metadata are captured to form a customized workflow to minimize performance by the user of redundant tasks.