Showing papers on "Meta Data Services published in 2021"

Complex Adaptive Systems in Technical Services: A Functional Model for Assessing Institutional Alignment

Abstract: As the demand for new services strains library resources, directors of research libraries must practice efficient cost management and demonstrate alignment with institutional objectives. For technical services, this requires managing the effective cost of metadata services, assessing core functions, and evaluating operational performance. This paper uses Complex Adaptive Systems (CASs) as a framework to expose the network of local and global dependencies that currently define the field of operation for technical services. Comparative analyses using a CASs framework were conducted on reports by the Library of Congress, the Heads of Technical Services in Large Research Libraries Interest Group, and the British Library. Each report addresses financial pressures placed on bibliographic control services in response to the 2008 recession. Statements within the reports were assigned to one of three dominant systems: bibliographic control, institutional identification, and distributive networks. The statements were then mapped to the CASs characteristics to determine environmental pressures and areas of adaptation. The reports exposed long-standing dependencies that tie local bibliographic control to a complex network of external agencies. Institutional shifts toward user-centered services coupled with growing fiscal restraint has disrupted the stability of these networks. The analyses found that in all cases network instability led to localized institutional adaptation to existing economic pressures. The paper recommends applying a CASs model to assess the alignment of distributed metadata standards and systems development to local institutional objectives.

Metadata Management Algorithm Based on Improved LSM Tree

Abstract: Hadoop distributed file system (HDFS) is one of the cores of Hadoop, but because HDFS storage and management of data capacity is limited by the memory size of NameNode, its scalability is constrained. In this article, we analyze two problems when NameNode manages metadata: loading FSImage takes too long and the capacity is limited by memory size. We propose optimizing the HDFS hierarchical metadata structure into a flat structure and removing metadata from memory. To this end, we design the F-HDFS based on improved log-structured merge-tree (LSM tree) and memory-mapped file for metadata management and introduce the F-HDFS metadata operations. In addition, F-HDFS is also compatible with features such as high availability of HDFS and snapshot, so that F-HDFS can be applied to existing HDFS-based applications. We implement the F-HDFS prototype system and compare it with HDFS. The results show that F-HDFS performance is better than HDFS for providing stable and fast metadata services.