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Concurrent multiresponse non-linear screening: Robust profiling of webpage performance

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  • Besseris, George J.

Abstract

Profiling engineered data with robust mining methods continues attracting attention in knowledge engineering systems. The purpose of this article is to propose a simple technique that deals with non-linear multi-factorial multi-characteristic screening suitable for knowledge discovery studies. The method is designed to proactively seek and quantify significant information content in engineered mini-datasets. This is achieved by deploying replicated fractional-factorial sampling schemes. Compiled multi-response data are converted to a single master-response effectuated by a series of distribution-free transformations and multi-compressed data fusions. The resulting amalgamated master response is deciphered by non-linear multi-factorial stealth stochastics intended for saturated schemes. The stealth properties of our method target processing datasets which might be overwhelmed by a lack of knowledge about the nature of reference distributions at play. Stealth features are triggered to overcome restrictions regarding the data normality conformance, the effect sparsity assumption and the inherent collapse of the ‘unexplainable error’ connotation in saturated arrays. The technique is showcased by profiling four ordinary controlling factors that influence webpage content performance by collecting data from a commercial browser monitoring service on a large scale web host. The examined effects are: (1) the number of Cascading Style Sheets files, (2) the number of JavaScript files, (3) the number of Image files, and (4) the Domain Name System Aliasing. The webpage performance level was screened against three popular characteristics: (1) the time to first visual, (2) the total loading time, and (3) the customer satisfaction. Our robust multi-response data mining technique is elucidated for a ten-replicate run study dictated by an L9(34) orthogonal array scheme where any uncontrolled noise embedded contribution has not been necessarily excluded.

Suggested Citation

  • Besseris, George J., 2015. "Concurrent multiresponse non-linear screening: Robust profiling of webpage performance," European Journal of Operational Research, Elsevier, vol. 241(1), pages 161-176.
    • Handle: RePEc:eee:ejores:v:241:y:2015:i:1:p:161-176
    DOI: 10.1016/j.ejor.2014.06.021
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    References listed on IDEAS

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    1. Wang, Tai-Yue & Huang, Chien-Yu, 2007. "Improving forecasting performance by employing the Taguchi method," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1052-1065, January.
    2. Pantula, Sastry G., 2011. "Statistics: A Key to Innovation in a Data-Centric World!," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 1-5.
    3. Asllani, Arben & Lari, Alireza, 2007. "Using genetic algorithm for dynamic and multiple criteria web-site optimizations," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1767-1777, February.
    4. Lin, Chang-Chun, 2006. "Optimal Web site reorganization considering information overload and search depth," European Journal of Operational Research, Elsevier, vol. 173(3), pages 839-848, September.
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    Cited by:

    1. Wang, Guodong & Shao, Mengying & Lv, Shanshan & Kong, Xiangfen & He, Zhen & Vining, Geoff, 2022. "Process parameter optimization for lifetime improvement experiments considering warranty and customer satisfaction," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

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