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Process Mining: Data Science in Action, 2
- Publisher
- Emerald Publishing
- Copyright
- © Emerald Publishing Limited
- ISSN
- 0265-671X
- DOI
- 10.1108/ijqrm-07-2022-0211
- Publisher site
- See Article on Publisher Site
Abstract
As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website.Design/methodology/approachPROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.FindingsThis article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits.Originality/valueIn this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.
Journal
International Journal of Quality & Reliability Management – Emerald Publishing
Published: Nov 14, 2024
Keywords: Process mining; Web mining; Web usability; Event log; Analysis