An E-Business Event Stream Mechanism for Improving User Tracing Processes
Source abstract
Abstract
With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Customer churn and segmentation · Data Stream Mining Techniques · Recommender Systems and Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (53)
Show source-provided references
1. A framework for analyzing international business and legal ethical standards10.1016/j.bushor.2018.07.003 ↗
2. Measurement systems of individual E-business competency in an E-business management environment
3. Social commerce in e-business of Pakistan: Opportunities, challenges and solutions
4. Business social media analytics: Characterization and conceptual framework10.1016/j.dss.2018.03.004 ↗
5. M. Thakur, P. Anadure and H. Saini, “Log file analyzer,” Jaypee University of Information Technology, 2019. [Online]. Available: http://ir.juit.ac.in/123456789/22855.
6. Comprehensive analysis of web log files for mining
7. Improved user navigation pattern prediction technique from web log data10.1016/j.proeng.2012.01.838 ↗
8. Optimization, security, privacy and trust in e-business systems10.1016/j.jcss.2014.12.008 ↗
9. Cloud computing adoption framework: A security framework for business clouds10.1016/j.future.2015.09.031 ↗
10. Alignment of teacher’s plan and students’ use of LMS resources. analysis of Moodle logs
11. Security design for e-business applications
12. Analysis of web logs and web user in web mining10.5121/ijnsa.2011.3107 ↗
13. Extracting knowledge from web server logs using web usage mining
14. A survey on preprocessing of web-log data in web usage mining
15. Identifying user behavior by analyzing web server access log file
16. Web usage mining: A survey on preprocessing of web log file
17. N. Niraula, “Web log data analysis: Converting unstructured web log data into structured data using Apache Pig,” M. S. dissertation, Cloud State University, 2017. [Online]. Available: https://repository.stcloudstate.edu/csit_etds/19.
18. Using hidden Markov Model to predict the surfing user’s intention of cyber purchase on the web
19. Mohammadifard · Modeling User Behavior from E-Commerce Data with Hidden Markov Models and Logistic Regression · 2013 · 1
20. Analysis of web server logs to understand internet user behavior and develop digital marketing strategies
21. Web-logs prediction with web mining
22. Analysis of visitor’s behavior from web log using web log expert tool
23. Practical aspects of log file analysis for E-commerce
24. Extracting users’ interests from web log data
25. Business process mining from E-commerce web logs
26. Web user navigation pattern mining approach based on graph partitioning algorithm
27. Clustering social audiences in business information networks10.1016/j.patcog.2019.107126 ↗
28. E-business application recommendation for SMEs based on organization profile using random forest classification
29. An empirical comparison of classification techniques for next event prediction using business process event logs10.1016/j.eswa.2019.04.016 ↗
30. An ensemble-based approach to the security-oriented classification of low-level log traces10.1016/j.eswa.2020.113386 ↗
31. Analysis of web log server files of E-commerce websites to study customer behavior pattern10.22214/ijraset.2017.4249 ↗
32. LTL formula patterns for enhancing the performance analysis of E-business structure
33. Reasoning on LTL on finite traces: Insensitivity to infiniteness
34. Analysis of users’ behavior in structured e-commerce websites10.1109/ACCESS.2017.2707600 ↗
35. Intruder detector: A continuous authentication tool to model user behavior
36. Design and implementation of secured E-business structure with LTL patterns for user behavior prediction
37. Sakar · Online Shoppers Purchasing Intention · 2018
38. Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks10.1007/s00521-018-3523-0 ↗
39. Kaggle, “Instacart Market Basket Analysis,” 2018. [Online]. Available: https://www.kaggle.com/c/instacart-market-basket-analysis/data.
40. An effective web page reorganization through heap tree and farthest first clustering approach
41. A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (EM) and PCA-ANFIS10.1016/j.elerap.2015.08.004 ↗
42. Fast and stable clustering analysis based on grid-mapping K-means algorithm and new clustering validity index10.1016/j.neucom.2019.07.048 ↗
43. A survey on various classification techniques for clinical decision support system10.5120/20498-2369 ↗
44. Intelligence system for diagnosis level of coronary heart disease with K-star algorithm10.4258/hir.2016.22.1.30 ↗
45. An efficient filtered classifier for classification of unseen test data in text documents
46. Efficiency of data mining models to predict academic performance and a cooperative learning model
47. Class-specific features using J48 classifier for text classification
48. Logistic model tree for human activity recognition using smartphone-based inertial sensors
49. Density-based clustering with side information and active learning
50. Meta-feature based data mining service selection and recommendation using machine learning models
51. Predictors of tuberculosis: Application of a logistic regression model10.1016/j.genrep.2019.100527 ↗
52. Analyzing the stock market based on the structure of KNN network10.1016/j.chaos.2018.05.018 ↗
53. A comparison of random forest variable selection methods for classification prediction modeling10.1016/j.eswa.2019.05.028 ↗