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Monitoring web QoE based on analysis of client-side measures and user behavior

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Abstract

An increasing number of web applications in all fields for different types of use incites developers and researchers to enhance the web applications as well as the network conditions to satisfy the final user. That’s why the estimation of the Quality of Experience for web applications (web QoE) remains necessary. The web QoE gives service and content providers an idea about the perceived quality by users as it helps them to determine issues for improvement. Waiting time influences user satisfaction. For this reason, we found many studies in web QoE interested in the first part of the browsing operation, such as the waiting time until the first page is completely loaded. However, these measures do not include the interactions of the user with the web application lately. We measured the interactions of the user with the web application via the user engagement metrics. This new field of research has attracted many researchers these last years due to its efficacity in determining the satisfaction level of the user. The contribution of our study is the research and use of user engagement metrics for QoE prediction. We have noticed that user engagement metrics (generally used to evaluate a user’s commitment in social media) are more precise in expressing the QoE of the user. For this reason, we used user engagement metrics to predict user web QoE that is the novelty of our work. In this study, we elaborated our dataset, which contains the three types of measurements; Quality of Service (QoS), QoE, and user engagement metrics. The obtained dataset reflects the user experience from several perspectives (the network quality, the loading process, and the interaction of the user with the web application). This reflection makes our dataset an exhaustive one. After collecting the data, we visualized our different metrics. Besides, we tried to predict the Mean Opinion Score (MOS) with Machine Learning (ML) algorithms, but we obtained low accuracy due to the small number of lines in our dataset. Finally, we tried to profile the users using the K-means clustering algorithm. In this clustering, we recuperated three user information metrics (age, gender, and study level).

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References

  1. Abdelwahed N, A BL, Asmi SE (2021) How user engagement metrics ameliorate the web qoe? Wirel Pers Commun 117(3):2383–2402

    Article  Google Scholar 

  2. Ahmed H, Jilani TA, W H (2017) Establishing standard rules for choosing best kpis for an e-commerce business based on google analytics and machine learning technique. Int J Adv Comput Sci Appl 8:562–567

    Google Scholar 

  3. Alaoui S, EL Bouzekri Y, EL Idrissi, Ajhoun R (2015) Building rich user profile based on intentional perspective. Procedia Comput Sci 73:342–349

    Article  Google Scholar 

  4. Alfian G, Syafrudin M, M FI (2018) A personalized healthcare monitoring system for diabetic patients by utilizing ble-based sensors and real-time data processing. Sensors, 18

  5. Allioui YE, Beqqali OE (2012) User profile ontology for the personalization approach. Int J Comput Applic 41:31–40

    Article  Google Scholar 

  6. Alreshoodi M, Woods J (2013) Survey on qoe-qos correlation models for multimedia services. International Journal of Distributed and Parallel Systems (IJDPS), 4

  7. Anitha V, P I (2016) A survey on predicting user behavior based on web server log files in a web usage mining. In: International conference on computing technologies and intelligent data engineering (ICCTIDE’16), pp 1–4

  8. Asrese AS, Walelgne EA, Bea V (2019) Measuring web quality of experience in cellular networks. In: International conference on passive and active network measurement (PAM 2019), pp 18–33

  9. Asrese AS, Eravuchira SJ, VBPS, Ott J (2019) Measuring web latency and rendering performance: method, tools, and longitudinal dataset. IEEE Trans Netw Serv Manag 16:535–549

    Article  Google Scholar 

  10. Attfield S, Kaza G , M L, Piwowarski B (2011) Towards a science of user engagement (position paper). In: WSDM’11

  11. Aung WT, Myanmar Y, K H (2009) Random forest classifier for multicategory classification of web pages. In: IEEE Asia-Pacific services computing conference (APSCC), pp 372–376

  12. Bakaev M, Speicher M, S H, Gaedke M (2020) I don’t have that much data! reusing user behavior models for websites from different domains. In: International conference on web engineering (ICWE), pp 146–162

  13. Barakovic S, Skorin-Kapov L (2017) Survey of research on quality of experience modelling for web browsing. Quality and User Experience, 2

  14. Bernaschina C, Brambilla M, A M, Umuhoza E (2017) A big data analysis framework for model-based web user behavior analytics. In: International conference on web engineering (ICWE), pp 98–114

  15. Bocchi E, L DC, Rossi D (2016) Measuring the quality of experience of web users. In: ACM SIGCOMM

  16. Brutlag J, Z A, Meenan P (2011) Above the fold time: measuring web page performance visually. In: Web performance and operations conference

  17. Calegari S, Pasi G (2010) Ontology-based information behaviour to improve web search. Future Internet 2:533–558

    Article  Google Scholar 

  18. Chikhaoui B, Wang TX, Pigot H (2014) Pattern-based causal relationships discovery from event sequences for modeling behavioral user profile in ubiquitous environments. Inf Sci 285:204–222

    Article  Google Scholar 

  19. Chowdhary ChL, Patel PV, K JK (2020) Analytical study of hybrid techniques for image encryption and decryption. Sensors, 20

  20. Ducasse J, M K, Pucihar KC (2020) Interactive web documentaries: a case study of audience reception and user engagement on iotok. Int J Human-Comput Interact 36:1558–1584

    Article  Google Scholar 

  21. Fang W (2007) Using google analytics for improving library website content and design: a case study. Libr Philos Pract, 1–17

  22. Fiedler M, Hossfeld T, P T-G (2010) A generic quantitative relationship between quality of experience and quality of service. In: Network Mag Glob Internetwkg, pp 36–41

  23. Gao Q, P D, Ahammad P (2017) Perceived performance of top retail webpages in the wild: insights from large-scale crowdsourcing of above-the-fold qoe. In: proceedings of the 2017 SIGCOMM Internet-QoE workshop

  24. Gauch S, Speretta M, A C, Micarelli A (2007) User profiles for personalized information access. In: The adaptive web: methods and strategies of web personalization. Springer, Berlin, pp 54–89

  25. Godoy D, Amandi A (2005) User profiling in personal information agents:a survey. Knowl Eng Rev 0:1–33

    Google Scholar 

  26. Grover P, Kar AK (2018) User engagement for mobile payment service providers - introducing the social media engagement model. J Retail Consum Serv, 53

  27. Gunter U, Onder I (2016) Forecasting city arrivals with google analytics. Ann Tour Res 61:199–212

    Article  Google Scholar 

  28. Hameed A, B B (2016) A decision-tree-based perceptual video quality prediction model and its application in fec for wireless multimedia communications. In: IEEE Trans Multimed, pp 764–774

  29. Hobfeld T (2020) From qos distributions to qoe distributions: a system’s perspective. In: IEEE Conference on network softwarization (IEEE NetSoft 2020)

  30. Ijaz MF, M S, Rhee J (2018) Hybrid prediction model for type 2 diabetes and hypertension using dbscan-based outlier detection, synthetic minority over sampling technique (smote), and random forest. Applied sciences, 8

  31. Ijaz MF, M A, Son Y (2020) Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors, 20

  32. Islam MJ, Wu QMl, Mama S-A (2007) Investigating the performance of naive-bayes classifiers and k- nearest neighbor classifiers. In: Convergence information technology, international conference, pp 1541–1546

  33. Jang Ch, Chang H, H A (2011) Profile for effective service management on mobile cloud computing. In: International conference on advanced communication and networking (ACN 2011). Springer, Berlin, pp 139–145

  34. Jahromi HZ, D TD, Hines A (2019) Establishing waiting time thresholds in interactive web mapping applications for network qoe management. In: 2019 30th Irish signals and systems conference (ISSC), pp 1–7

  35. Jahromi HZ, D TD, Hines A (2020) Beyond first impressions: estimating quality of experience for interactive web applications. IEEE Access 8:47741–47755

    Article  Google Scholar 

  36. Jahromi HZ, D TD, Hines A (2020) How crisp is the crease? A subjective study on web browsing perception of above-the-fold. In: 6th IEEE Conference on network softwarization (NetSoft 2020)

  37. Kang Y, Chen H, L X (2013) An artificial-neural-network-based qoe estimation model for video streaming over wireless networks. In: IEEE/CIC international conference on communications in China (ICCCC): QRS: QoS, reliability and security, pp 764–774

  38. Kanoje S, S G, Mukhopadhyay D (2014) User profiling trends, techniques and applications. International Journal of Advance Foundation and Research in Computer (IJAFRC), 1

  39. Katsarakis M, Fortetsanakis G, P C (2014) On user-centric tools for qoe-based recommendation and real-time analysis of large-scale markets. IEEE Commun Mag, 37–43

  40. Kawazu H, Toriumi F, M T (2016) Analytical method of web user behavior using hidden markov model. In: IEEE International conference on big data (Big Data), pp 2518–2524

  41. Khan A, Sun EJ , E I (2010) Quality of experience driven adaptation scheme for video applications over wireless networks. In: IET Commun, pp 1337–1347

  42. Kumar A, J S, Li H (2019) Stages of user engagement on social commerce platforms: analysis with the navigational clickstream data. Int J Electron Commer 23:179–211

    Article  Google Scholar 

  43. Lashkari AH, Ghorbani MC, A A (2019) A survey on user profiling model for anomaly detection in cyberspace. J Cyber Secur Mob 8:75–112

    Article  Google Scholar 

  44. Le Callet P, Viard-Gaudin C, D B (2006) A convolutional neural network approach for objective video quality assessment. In: IEEE Trans Neural Netw, pp 1316–1327

  45. Le Callet P, S M, Perkis A (2012) Qualinet white paper on definitions of quality of experience, European network on quality of experience in multimedia systems and services (COST Action IC 1003), 3

  46. Lei X (2018) Modeling and intelligent analysis of web user behavior. In: International conference on engineering simulation and intelligent control (ESAIC), pp 192–195

  47. Letaifa AB (2019) Wbqoems: web browsing qoe monitoring system based on prediction algorithms. Int J Commun Syst, 32

  48. Machado VA, Silva CN (2011) A new proposal to provide estimation of qos and qoe over wimax networks. In: IEEE Third Latin-American conference on communications

  49. Mason L, Baxter J, PB MF (1999) Boosting algorithms as gradient descent. In: International conference on neural information processing systems, pp 512–518

  50. Menkovski V, A L (2010) Machine learning approach for quality of experience aware networks. In: International conference on intelligent networking and collaborative systems, pp 461–466

  51. Mok RKP, G K, Claffy KC (2019) Quince: a unified crowdsourcing-based qoe measurement platform. In: Proc. SIGCOMM Conf., pp 60–62

  52. Orsolic I, L S-K, Suznjevic M (2017) Towards a framework for classifying youtube qoe based on monitoring of encrypted traffic. In: Proc. International summit of young researchers on the quality of experience in emerging multimedia services (QEEMS 2017) Conf., pp 1–5

  53. Ouaftouh S, A Z, Idri A (2015) User profile model: a user dimension based classification. In: 10th International conference on intelligent systems: theories and applications (SITA 2015), pp 1–5

  54. Pal M, P MM (2002) A comparison of decision tree and back propagation neural network classifiers for land use classification. In: IEEE International geoscience and remote sensing symposium c’IGARSS, pp 503–505

  55. Panigrahi R, Borah S, A KB (2021) A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets. Mathematics, 9

  56. Panigrahi R, Borah S, A KBMFI (2021) Performance assessment of supervised classifiers for designing intrusion detection systems: a comprehensive review and recommendations for future research. Mathematics, 9

  57. Plaza B (2011) Google analytics for measuring website performance. Tour Manag 32:477–481

    Article  Google Scholar 

  58. Ramawat S, Das D (2017) Web usage mining for understanding user behavior. Int J Comput Eng Technol (IJCET) 8:12–22

    Google Scholar 

  59. Rohloff T, Oldag S, J R, Meinel C (2019) Utilizing web analytics in the context of learning analytics for large-scale online learning. In: IEEE Global engineering education conference (EDUCON), pp 296–305

  60. Sachse J (2019) The influence of snippet length on user behavior in mobile web search an experimental eye-tracking study. Aslib J Inform Manag 71:325–343

    Article  Google Scholar 

  61. Shahid M, Rossholm A, B L (2013) A no-reference machine learning based video quality predictor. In: International workshop on quality of multimedia experience (QoMEX), pp 176–181

  62. Sahu S, R G, Dutta A (2019) An analysis of web user behavior using hybrid algorithm based on sequential pattern mining. Int J Appl Eng Res 14:2339–2346

    Google Scholar 

  63. Salutari F, Hora DD, G D, Rossi D (2019) A large-scale study of wikipedia users’ quality of experience. In: The World Wide Web Conference (WWW’19), pp 3194–3200

  64. Samet N, BenLetaifa A, M HST (2016) Real-time user experience evaluation for cloud-based mobile video. In: International conference on advanced information networking and applications workshops (WAINA), pp 204–208

  65. Strohmeier D, S J-P, Raake A (2012) Toward task-dependent evaluation of web-qoe:free exploration vs. “who ate what?”. In: Proc. GC’12 Workshop: quality of experience for multimedia communications conf., pp 1309–1313

  66. Tamang J, Nkapkop JDD, F I (2021) Dynamical properties of ion-acoustic waves in space plasma and its application to image encryption. IEEE Access 9:18762–18782

    Article  Google Scholar 

  67. Thushara Y, Ramesh V (2016) A study of web mining application on e-commerce using google analytics tool. Int J Comput Applic 149:21–26

    Article  Google Scholar 

  68. Upadhyaya B, Zou Y, I K, Ng J (2014) Quality of experience: what end-users say about web services?. In: International Conference on Web Services (ICWS 2014), pp 57–64

  69. Wang Z, Jain A (2012) Navigation timing. In: W3C Recommendation

  70. Wassermann S, Casas P, MS (2020) How good is your mobile (web) surfing? Speed index inference from encrypted traffic. ACM SIGCOMM 2020 Posters, Demos, and Student Research Competition

  71. Yang Y (2010) Web user behavioral profiling for user identification. Decis Support Syst 49:261–271

    Article  Google Scholar 

  72. Zhang X, Sen S, D K (2019) E2e: embracing user heterogeneity to improveality of experience on the web. In: Proc. SIGCOMM Conf., pp 289–302

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Authors and Affiliations

  1. Cosim Lab, Supcom Tunisia, University of Carthage, Tunis, Tunisia

    Nawres Abdelwahed & Sadok El Asmi

  2. Mediatron Lab, Supcom Tunisia, University of Carthage, Tunis, Tunisia

    Asma Ben Letaifa

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  1. Nawres Abdelwahed
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Correspondence to Nawres Abdelwahed.

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Abdelwahed, N., Letaifa, A.B. & Asmi, S.E. Monitoring web QoE based on analysis of client-side measures and user behavior. Multimed Tools Appl 82, 6243–6269 (2023). https://doi.org/10.1007/s11042-022-13427-5

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