A deep learning approach for predicting critical events using event logs

Huang, Congfang; Deep, Akash; Zhou, Shiyu; Veeramani, Dharmaraj

doi:10.1002/qre.2853

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…16 These systems are specifically designed to raise alarms or monitoring tickets whenever an anomaly in the system is observed (i.e., Usage warning, Disk Full, etc.). A number of solutions are available in literature [17][18][19][20] which can automatically analyze this type of system generated monitoring tickets and event logs. In contrast, the proposed system can be used for user generated ticket as well as for system generated tickets.…”

Section: Related Workmentioning

confidence: 99%

A multiapproach generalized framework for automated solution suggestion of support tickets

Zaidi

Fraz

Shahzad

et al. 2021

Int J of Intelligent Sys

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Nowadays, customer support systems are one of the key factors in maintaining any big company's reputation and success. These systems are capable of handling a large number of tickets systemically and provides a mechanism to track/logs the communication between customer and support agents. Companies invest huge amounts of money in training support agents and deploying customer care services for their products and services. Support agents are responsible for handling different customer queries and implementing required actions to solve a particular issue or problem raised by the service/product user. In a bigger picture, customer support systems could receive a large amount of ticket raised depending upon the number of users and services being offered. Customer care service gets directly affected due to the high volume of tickets and a limited number of support agents. Therefore, providing support agents with the recommendations about the possible resolution actions for a new ticket would be helpful and can save a lot of time. This study is focused on the development of an end‐to‐end framework for suggesting resolution actions rather than recommending free form resolution text against a newly raised ticket. To develop such a system, the pipeline is broadly divided into four components that are data preprocessing, actions extractor, resolution predictor, and evaluation. In actions extractor module, we have proposed a technique to identify and extract actionable phrases from resolution text. For resolution predictor, we have proposed two different pipelines that are referred as “Similarity Search Model” and “End‐to‐End Model.” The similarity search method is based on a ticket similarity search to find the most relevant historical tickets which then leads to corresponding resolution actions. On the other hand, end‐to‐end model make use of actions extractor module directly and implemented in a way to directly predict resolution actions. To compare and evaluate the mentioned methods on the same ground, we also proposed an actions evaluation criterion which uses BertScore and METEOR score jointly to compute the score against actual and predicted actions for a particular test ticket. The analysis and experiments are performed on the real‐world IBM ticket data set. Overall, we observed that end‐to‐end model outperformed similarity search‐based methods and achieved better performance and scores comparatively. The trained models and code are available at https://bit.ly/2GbUBVk.

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Section: Related Workmentioning

confidence: 99%

A multiapproach generalized framework for automated solution suggestion of support tickets

Zaidi

Fraz

Shahzad

et al. 2021

Int J of Intelligent Sys

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Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Sim

Sutrisnowati²,

Won³

et al. 2022

IEEE Access

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Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events

2023

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Maintenance processes are of high importance for industrial plants. They have to be performed regularly and uninterruptedly. To assist maintenance personnel, industrial sensors monitored by distributed control systems observe and collect several machinery parameters in the cloud. Then, machine learning algorithms try to match patterns and classify abnormal behaviors. This paper presents a new deep learning model called stranded-NN. This model uses a set of NN models of variable layer depths depending on the input. This way, the proposed model can classify different types of emergencies occurring in different time intervals; real-time, close-to-real-time, or periodic. The proposed stranded-NN model has been compared against existing fixed-depth MLPs and LSTM networks used by the industry. Experimentation has shown that the stranded-NN model can outperform fixed depth MLPs 15–21% more in terms of accuracy for real-time events and at least 10–14% more for close-to-real-time events. Regarding LSTMs of the same memory depth as the NN strand input, the stranded NN presents similar results in terms of accuracy for a specific number of strands. Nevertheless, the stranded-NN model’s ability to maintain multiple trained strands makes it a superior and more flexible classification and prediction solution than its LSTM counterpart, as well as being faster at training and classification.

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A deep learning approach for predicting critical events using event logs

Cited by 4 publications

References 22 publications

A multiapproach generalized framework for automated solution suggestion of support tickets

A multiapproach generalized framework for automated solution suggestion of support tickets

Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events

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