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

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

توضیحات محصول

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

INTRODUCTIN

In recent years, there has been an unprecedented increase in the quantity and variety of data generated worldwide. According to the IDC’s Digital Universe study, the world’s information is doubling every two years and is predicted to reach 40ZB by This increase in data, often referred to as a “data tsunami”, is driven by the proliferation of social media along with an increase in mobile and networked devices (the Internet of Things), finance and online retail as well as advances in the physical and life sciences sectors. As evidence of this, the online microblogging service Twitter, processes approximately 2020 (Digital Universe Study (on behalf of EMC Corporation) [1]).
12 TB of data per day, while Facebook receives more than five hundred million likes per day (McKinsey Global Institute [2]). In addition, the Cisco Internet Business Solutions Group (IBSG) predicts that there will be 25 billion devices connected to the Internet by 2015 and 50 billion by 2020 (Cisco Internet Business Solutions Group
(IBSG) [3]). Such vast datasets are commonly referred to as “Big Data”. Big Data is characterised not only by its volume, but by a rich mix of data types and formats (variety) and it’s time sensitive nature which marks a deviation from traditional batch processing (velocity) (Karmasphere [4]). These characteristics are commonly referred to as the 3 V’s

Year : 2014

Publisher : Springer

By : Arantxa Duque Barrachina and Aisling O’Driscoll

File Information : English Language/11 Page /Size :1.5 M

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سال : 2014

ناشر : Springer

کاری از :Arantxa Duque Barrachina and Aisling O’Driscoll

اطلاعات فایل : زبان انگلیسی /11 صفحه /حجم : 1.5 M

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