A big data methodology for categorising technical support requests using Hadoop and Mahout

Arantxa Duque Barrachina; Aisling O’Driscoll

doi:10.1186/2196-1115-1-1

A big data methodology for categorising technical support requests using Hadoop and Mahout

Arantxa Duque Barrachina
, Aisling O’Driscoll

Research output: Contribution to journal › Article › peer-review

Abstract

Technical Support call centres frequently receive several thousand customer queries on a daily basis. Traditionally, such organisations discard data related to customer enquiries within a relatively short period of time due to limited storage capacity. However, in recent years, the value of retaining and analysing this information has become clear, enabling call centres to identify customer patterns, improve first call resolution and maximise daily closure rates. This paper proposes a Proof of Concept (PoC) end to end solution that utilises the Hadoop programming model, extended ecosystem and the Mahout Big Data Analytics library for categorising similar support calls for large technical support data sets. The proposed solution is evaluated on a VMware technical support dataset.

Original language	English
Article number	1
Journal	Journal of Big Data
Volume	1
Issue number	1
DOIs	https://doi.org/10.1186/2196-1115-1-1
Publication status	Published - 1 Dec 2014
Externally published	Yes

Keywords

Big data analytics
Distributed clustering
Hadoop
HBase
Hive
Mahout
Parallelised programming
Support call center

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10.1186/2196-1115-1-1

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A big data methodology for categorising technical support requests using Hadoop and Mahout

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Keywords

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