Meta Data Services

About: Meta Data Services is a research topic. Over the lifetime, 2564 publications have been published within this topic receiving 40102 citations.

An efficient dynamic indexing and metadata based storage in cloud environment

Abstract: Cloud computing is an emerging, computing model wherein the tasks are allocated to software, combination of connections, and services accessed over a network. This connections and network of servers is collectively known as the cloud. In place of operating their own data centers, users might rent computing power and storage capacity from a service provider and pays only for what they use. Cloud storage is delivering the data storage as service. If the data is stored in cloud, it must provide the data access and heterogeneity. With the advances in cloud computing it allows storing of large number of images and data throughout the world. This paper proposes the indexing and metadata management which helps to access the distributed data with reduced latency. The metadata management can be enhanced for large scale file system applications. When designing the metadata, the storage location of the metadata and attributes is important for the efficient retrieval of the data. Indexes are used to quickly locate data without having to search over every location in storage. Based on these two models, the data can be easily fetched and the search time was reduced to retrieve the appropriate data.

Metadata for Evidence Based Medicine resources

Abstract: A new metadata element set based on Dublin Core Metadata Element Set (DC) and Admin-Core: Administrative Container Core (A-Core) was proposed for Evidence Based Medicine (EBM) sources after reviewing metadata elements and contents of current EBM sources and medical metadata for Internet resources. The metadata schema was designed to provide a common format for existing primary and secondary studies; further for Internet resources as prospective sources. An enhanced DC.Description element can store structured abstracts of primary studies in primary and secondary studies of clinical research; A-Core elements are used for indexers or creators of metadata for primary studies. Two encoding schemes were suggested as EBM qualifiers for the DC.Subject element to distinguish important factors of EBM practices: the degree of evidence and focuses of clinical perspectives such as therapy, diagnosis, prognosis, and etiology. An additional feature of this metadata schema is in distinction of a variety of "types" (e.g., study type, resource type, format, genre)

Research of Metadata Based Digital Educational Resource Sharing

Abstract: Metadata is the data that describes, locates and manages a specific resource object, whose discovering and obtaining is meanwhile facilitated. The application and popularization of metadata standard is propitious to the normalized description and sharing of resources. There are various metadata standards because of the diversity of resources and different demands of application, which hinders the share of resources. The present study first makes a brief introduction of some usual ways of metadata interoperation, such as metadata crosswalk, metadata open searching, semantic metadata interoperation etc., then presents a tentative research on metadata registry (MR) and automatic metadata extraction.

Getting dependency metadata using statement execution plans

Abstract: An execution plan for a database statement can be retrieved from a database server. References to objects can be identified in the execution plan, and metadata from the references can be assembled in a data structure on computer readable storage media. The metadata can reflect dependencies on the objects. Additionally, other dependency metadata can be augmented with the metadata from the references.

An Infrastructure for Acquiring High Quality Semantic Metadata

Abstract: Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a verification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation comparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata.