Decorating the cloud: enabling annotation management in MapReduce


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.

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    The assumption of a single InputFormat for a given dataset can be easily relaxed in CloudNotes by extending the OID object to maintain both the start and end offsets of the formed records. As such, annotations will be attached to “byte segments” within the file. Annotations can then propagate along with these byte segments independent from the used InputFormat.

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    The OIds passed to a reduce task are implemented using the same Iterator mechanism currently used for passing the values. Therefore, if the inputs’ size is too large to fit in memory, they are streamed from disk as needed in the same standard way.


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Lu, Y., Li, Y. & Eltabakh, M.Y. Decorating the cloud: enabling annotation management in MapReduce. The VLDB Journal 25, 399–424 (2016).

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  • Distributed annotation management
  • MapReduce
  • Cloud-based annotations