Research Article
MapReduce-Guided Scalable Compressed Dictionary Construction for Evolving Repetitive Sequence Streams
@INPROCEEDINGS{10.4108/icst.collaboratecom.2013.254135, author={Pallabi Parveen and Pratik Desai and Bhavani Thuraisingham and Latifur Khan}, title={MapReduce-Guided Scalable Compressed Dictionary Construction for Evolving Repetitive Sequence Streams}, proceedings={9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing}, publisher={ICST}, proceedings_a={COLLABORATECOM}, year={2013}, month={11}, keywords={mapreduce cloud sequence unsupervised learning}, doi={10.4108/icst.collaboratecom.2013.254135} }- Pallabi Parveen
Pratik Desai
Bhavani Thuraisingham
Latifur Khan
Year: 2013
MapReduce-Guided Scalable Compressed Dictionary Construction for Evolving Repetitive Sequence Streams
COLLABORATECOM
IEEE
DOI: 10.4108/icst.collaboratecom.2013.254135
Abstract
Users' repetitive daily or weekly activities may constitute user profiles. For example, a user's frequent command sequences may represent normative pattern of that user. To find normative patterns over dynamic data streams of unbounded length is challenging. For this, an unsupervised learning approach is proposed in our prior work by exploiting a compressed/quantized dictionary to model common behavior sequences. This work suffers scalability issues. Hence, in this paper, we propose and implement a MapReduce-based framework to construct a quantized dictionary. We show effectiveness of our distributed parallel solution on a benchmark dataset.
Copyright © 2013–2024 ICST