Nikhil Malhotra

dblp:225/4531 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0001-9517-1029ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Services computing and microservices · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 77% Knowledge representation and reasoning · 23%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
text classification
0.412019
Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI · AAAI 2019
Services computing and microservices › service management
IT service management
0.412019
Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI · AAAI 2019
Services computing and microservices › service management
ticket assignment
0.412019
Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI · AAAI 2019
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge-based systems › rule-based systems
rule-based reasoning
0.112019
Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI · AAAI 2019

Methods — techniques the papers use, named apart from their topics

rule engine · 0.8ensemble classifier · 0.8
YearPublicationVenuePosition
2019 Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI
abstract
Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of a classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum. Till date, our deployed system has already served more than 700,000 tickets in production.
Atri Mandal, Nikhil Malhotra, Shivali Agarwal, Anupama Ray, Giriprasad Sridhara
AAAI2
2019 Improving IT Support by Enhancing Incident Management Process with Multi-modal Analysis
Atri Mandal, Shivali Agarwal, Nikhil Malhotra, Giriprasad Sridhara, Anupama Ray, Daivik Swarup
ICSOC3
2018 Cognitive System to Achieve Human-Level Accuracy in Automated Assignment of Helpdesk Email Tickets
Atri Mandal, Nikhil Malhotra, Shivali Agarwal, Anupama Ray, Giriprasad Sridhara
ICSOC2