Efficient e-Platform Ticket Solution using Beacons

Kumar, Chandan

doi:10.1109/icscti.2015.7489623

Cited by 2 publications

(5 citation statements)

References 0 publications

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“…Figure 2 shows the predicting ticket completion time using the quick/slow bins with the use of SVM together with an RBF Kernel. It shows high recall and precision (Kumar, 2015). The results are also the same with Figure 3 even with the use of the XGBoost tool.…”

Section: Figure 2 Data Table Predicting Fast and Slow Issue Completio...supporting

confidence: 72%

“…Another is ticket resolution techniques create delays. Manually translating raw technical data into reports that make sense to management is time-consuming and hinders productivity (Kumar, 2015).…”

Section: Research Journal Filteringmentioning

confidence: 99%

“…A 20% -80% data split is used for data testing and training. JIRA ticket completion time is predicted using two binning methods: the quick/slow approach and the novel binning approach that consists of the information found in Figure 1 (Kumar, 2015).…”

Section: Figure 1 Data Table Of Issue Completion Time Binsmentioning

confidence: 99%

“…Figure 4 shows the most weighted features which are shown in the table. These features are essential in the JIRA ticketing system and are being raised by users of the issue without writing any notes or comments as it doesn't play any part in predicting the completion time of a specific issue (Kumar, 2015).…”

Section: Figure 2 Data Table Predicting Fast and Slow Issue Completio...mentioning

confidence: 99%

“…Project stakeholders can control the information being relayed from one department to another which includes the filtering of issue types to be assigned in each department. With each issue type, workflow schemes are also present where the invoice workflow, desired status types, and workflow transitions are configured (Kumar, 2015).…”

Section: Information Gatheringmentioning

confidence: 99%

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Ticketing System: A Descriptive Research on the Use of Ticketing System for Project Management and Issue Tracking in IT Companies

Aglibar¹,

Alegre²,

Mundo³

et al. 2023

IJCSR

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Purpose -IT companies are popular in the present time as technology arises in the entire world. Incidents and Requests for either clients or company internal are hard to manually track as it is a day-to-day transaction. To keep all the logs, IT Companies are using a ticketing system to track all the records they are gathering. One of the most popular ticketing systems is the JIRA. Either the support team or company internal is benefitting from the use of JIRA as it easily documents all the different transactions and keeps track of different incidents and requests. One of the cons of JIRA is its limited features. The researchers conducted research entitled "Ticketing System: A Descriptive Research on the Use of Ticketing System among Employees of ABC Company" where it aims to determine the effectiveness of JIRA as a ticketing system despite its limited features.Method -The researchers used a descriptive method to analyze the pros and cons of this ticketing system. The researchers also mentioned how this system works to provide knowledge to the audience that is using other types of ticketing systems. Conclusion -It is concluded that this JIRA Ticketing system is helpful for both support groups and internal members of the company as they track all the incidents and request tickets for their clients despite JIRA's limited features.Recommendation -The researchers strongly recommend to venture out and try other analytic models and start with the ones mentioned in this study for a good foundation and idea. This is can provide the research a new set of results that can be useful to both established and startup companies.Practical Implication -This research will benefit not only the established companies but also the start up companies that are currently struggling in choosing an efficient ticketing system that work well not only in ticketing management but also in project management as well.

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Section: Figure 2 Data Table Predicting Fast and Slow Issue Completio...supporting

confidence: 72%

Section: Research Journal Filteringmentioning

confidence: 99%

Section: Figure 1 Data Table Of Issue Completion Time Binsmentioning

confidence: 99%

Section: Figure 2 Data Table Predicting Fast and Slow Issue Completio...mentioning

confidence: 99%

Section: Information Gatheringmentioning

confidence: 99%

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