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Towards Distributed k-NN similarity for Scalable Case Retrieval

Authors:

Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

The Third Workshop on Synergies between CBR and Machine Learning

Abstract

In Big data era, the demand of processing large amount of data posing several challenges. One biggest challenge is that it is no longer possible to process the data in a single machine. Similar challenges can be assumed for case-based reasoning (CBR) approach, where the size of a case library is increasing and constructed using heterogenous data sources. To deal with the challenges of big data in CBR, a distributed CBR system can be developed, where case libraries or cases are distributed over clusters. MapReduce programming framework has the facilities of parallel processing massive amount of data through a distributed system. This paper proposes a scalable case-representation and retrieval approach using distributed k-NN similarity. The proposed approach is considered to be developed using MapReduce programming framework, where cases are distributed in many clusters.

Bibtex

@inproceedings{Barua5177,
author = {Shaibal Barua and Shahina Begum and Mobyen Uddin Ahmed},
title = {Towards Distributed k-NN similarity for Scalable Case Retrieval},
month = {July},
year = {2018},
booktitle = {The Third Workshop on Synergies between CBR and Machine Learning },
url = {http://www.es.mdu.se/publications/5177-}
}