Abstract
Despite the Information Technology (IT) era keeps evolving rapidly, many business operations are heavily relying on software application service to process the daily business transactions today. Therefore, software application service is unacceptable if it is unavailable during business hours. However, there are many possibilities can cause the software application service irresponsive. This is because the root cause can be possible to arise mostly in either within the software application layer, or other factors which are falling outside the software application layer. Under such complex situation, it is time consuming to identify the root cause and it is always unavoidable. The objective of Prescriptive Analytical Logic Model (PAL) is not focusing on only solving such problem. Indeed, it aims to minimize the duration spent on root cause analysis activities, and to decide the preferred resolution for the identified root cause. Hence, the algorithm inside the PAL has incorporated with Supervised Learning approach to identify the possible errors of the root cause, and adopted Analytic Hierarchy Process (AHP) to decide the preferred resolution based on the given priority to the errors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
da Silva Neves, A.J., Camanho, R.: The use of AHP for IT project priorization – a case study for oil & gas company. Procedia Comput. Sci. 55, 1097–1105 (2015). ScienceDirect. Elsevier. https://www.sciencedirect.com/science/article/pii/S1877050915015513. Accessed 20 Apr 2020
Gogus, A.: Learning cycles. In: Seel, N.M. (ed.) Encyclopedia of the Sciences of Learning. Springer, Boston (2012). https://doi.org/10.1007/978-1-4419-1428-6_143
Guru99: Unsupervised Machine Learning: What is, Algorithms, Example (2020). www.guru99.com. https://www.guru99.com/unsupervised-machine-learning.html. Accessed 24 Feb 2020
Hoo Meng, W., Amalathas, S.S.: A new approach towards developing a prescriptive analytical logic model for software application error analysis. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) CoMeSySo 2019 2019. AISC, vol. 1046, pp. 256–274. Springer, Cham (2019). ISBN:978-3-030-30328-0. https://doi.org/10.1007/978-3-030-30329-7_24
Wong, H.M., Amalathas, S.S.: An approach towards developing an algorithm for software application error analysis. David Publishing Company (2019). https://doi.org/10.17265/2328-2185/2019.04.006
Wong, H.M., Amalathas, S.S., Zitkova, T.: A business operation stability by improving the speed of restoration on software application service. David Publishing Company (2020). https://doi.org/10.17265/2328-2185/2020.01.008
Wong, H.M., Amalathas, S.S., Zitkova, T.: An approach towards designing a prescriptive analytical logic model for software application root cause analysis. Int. J. Adv. Res. Publ. (IJARP) (2019). http://www.ijarp.org/published-research-papers/nov2019/An-Approach-Towards-Designing-A-Prescriptive-Analytical-Logic-Model-For-Software-Application-Root-Cause-Analysis.pdf
Valdman, J.: Log File Analysis (2001). https://www.kiv.zcu.cz/site/documents/verejne/vyzkum/publikace/technicke-zpravy/2001/tr-2001-04.pdf. Accessed 18 Jan 2017
Pan, J.: Software Testing. Carnegie Mellon University (1999). http://www.sci.brooklyn.cuny.edu/~sklar/teaching/s08/cis20.2/papers/software-testing.pdf
Murínová, J.: Application Log Analysis (2015). http://is.muni.cz/th/374567/fi_m/thesis_murinova.pdf. Accessed 18 Jan 2017
Brownlee, J.: Linear Regression for Machine Learning. Machine Learning Mastery Pty. Ltd (2019). https://machinelearningmastery.com/linear-regression-for-machine-learning/. Accessed 24 Feb 2020
Klass, L.: Machine learning - definition and application examples. MM International (2018). https://www.spotlightmetal.com/machine-learning--definition-and-application-examples-a-746226/?cmp=go-aw-art-trf-SLM_DSA-20180820&gclid=Cj0KCQjwxYLoBRCxARIsAEf16-vBblTDXGp3-EuK0AsxQu90ckOqGbTyXEvjQBWSA3D3MHB51TxDhqUaAlPDEALw_wcB. Accessed 21 Feb 2020
Cmielowski, L.: Adoption of machine learning to software failure prediction. IBM Watson Data (2017). https://medium.com/ibm-data-science-experience/adoption-of-machine-learning-to-software-failure-prediction-e8d85ed0338f. Accessed 2 Apr 2019
Management Logic: Root-Cause Analysis (2012). http://www.management-logic.com/toolbox/sales/Root-Cause%20Analysis/Index.html. Accessed 28 Nov 2015
Wettergreen, M.: Pairwise Comparison Charts 2: Setting Up and Running Them [Video]. YouTube, 7 August 2014. https://www.youtube.com/watch?v=orrQFHKlocs&list=PLGVNLF9na6k7P2c-NQ_8CBEBbLw4pjzG9&index=7&t=0s. Accessed 28 Aug 2020
Vaidya, O.S., Kumar, S.: Analytic hierarchy process: an overview of applications. Department of Mechanical Engineering, Army Institute of Technology, Pune, National Institute of Industrial Engineering (NITIE), Vihar Lake, Mumbai (2004). http://ac.els-cdn.com/S0377221704003054/1-s2.0-S0377221704003054-main.pdf?_tid=39850adc-5d78-11e7-b5bf-00000aab0f26&acdnat=1498815840_e0c9a10c99c46ad30db8da4ef17e817b. Accessed 12 Sept 2018
Roman, V.: Unsupervised machine learning: clustering analysis. Towards Data Science. towardsdatascience.com (2019). https://towardsdatascience.com/unsupervised-machine-learning-clustering-analysis-d40f2b34ae7e. Accessed 9 Mar 2020
Saaty, R.W.: The analytic hierarchy process - what it is and how it is used. Pergamon Journals Ltd, Great Britain (1987). http://ac.els-cdn.com/0270025587904738/1-s2.0-0270025587904738-main.pdf?_tid=55956250-6b90-11e7-b75e-00000aab0f02&acdnat=1500365511_5a54a3ecf035ad3e45ab72b909b7632e. Accessed 12 Sept 2018
Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1) (2008). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.3124&rep=rep1&type=pdf. Accessed 12 Sept 2018
Peng, W.W., Wallace, D.R.: Software Error Analysis. NIST Special Publication (1993). http://www.geocities.ws/itopsmat/SoftwareErrorAnalysis.pdf. Accessed 12 Dec 2015
Wilson, A.: A brief introduction to supervised learning. Towards Data Science (2019). https://towardsdatascience.com/a-brief-introduction-to-supervised-learning-54a3e3932590. Accessed 21 Feb 2020
Wong, H.M., Amalathas, S.S.: The error analysis for enterprise software application using analytic hierarchy process and supervised learning: a hybrid approach on root cause analysis. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) CoMeSySo 2020. AISC, vol. 1295, pp. 633–643. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63319-6_59
Wong, H.M., Amalathas, S.: A new analysis approach incorporated with analytic hierarchy process for the software application in a multiple tiers environment. Eur. J. Electr. Eng. Comput. Sci. 3(6) (2019). https://doi.org/10.24018/ejece.2019.3.6.160
Wong, H.M., Amalathas, S.: The root cause analysis algorithm design incorporated with analytic hierarchy process for software application error. Eur. J. Electr. Eng. Comput. Sci. 4(1) (2020). https://doi.org/10.24018/ejece.2020.4.1.166
Wong, H.M., Amalathas, S., Zitkova, T.: A prescriptive logic model for software application root cause analysis. Eur. J. Electr. Eng. Comput. Sci. 3(5) (2019). https://doi.org/10.24018/ejece.2019.3.5.133
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wong, H.M., Amalathas, S.S., Ray, S.K. (2021). A Hybrid Approach and Implementation on Root Cause Analysis Logic Model for Enterprise Software Application. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Intelligent Systems. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-030-90321-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-90321-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90320-6
Online ISBN: 978-3-030-90321-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)