Data curation and annotation are indispensable mechanisms to a wide range of applications for capturing various types of metadata information. This metadata not only increases the data’s credibility and merit, and allows end users and applications to make more informed decisions, but also enables advanced processing over the data that is not feasible otherwise. That is why annotation management has been extensively studied in the context of scientific repositories, web documents, and relational database systems. In this paper, we make the case that cloud-based applications that rely on the emerging Hadoop infrastructure are also in need for data curation and annotation and that the presence of such mechanisms in Hadoop would bring value-added capabilities to these applications. We propose the “ CloudNotes ” system, a full-fledged MapReduce-based annotation management engine . CloudNotes addresses several new challenges to annotation management including: (1) scalable and distributed processing of annotations over large clusters, (2) propagation of annotations under the MapReduce’s blackbox execution model, and (3) annotation-driven optimizations ranging from proactive prefetching and colocation of annotations, annotation-aware task scheduling, novel shared execution strategies among the annotation jobs, and concurrency control mechanisms for annotation management. These challenges have not been addressed or explored before by the state-of-art technologies. CloudNotes is built on top of the open-source Hadoop/HDFS infrastructure and experimentally evaluated to demonstrate the practicality and scalability of its features, and the effectiveness of its optimizations under large workloads.
The VLDB Journal – Springer Journals
Published: Jun 1, 2016
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