Cilt 8 Sayı 1 (2020): Business & Management Studies: An International Journal
Makaleler

VERİ MADENCİLİĞİ YAKLAŞIMI İLE TELEKOMUNİKASYON SEKTÖRÜNDE ARIZA GİDERME ANALİZİ

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  • Burcu ORALHAN
Burcu ORALHAN
Dr. Öğr. Üyesi, Nuh Naci Yazgan Üniversitesi

Yayınlanmış 25.03.2020

Anahtar Kelimeler

  • Data Mining, Troubleshooting Analysis, Telecommunication Services
  • Veri Madenciliği, Arıza Analizi, Telekomünikasyon Hizmetleri

Nasıl Atıf Yapılır

VERİ MADENCİLİĞİ YAKLAŞIMI İLE TELEKOMUNİKASYON SEKTÖRÜNDE ARIZA GİDERME ANALİZİ. (2020). Business & Management Studies: An International Journal, 8(1), 1026-1043. https://doi.org/10.15295/bmij.v8i1.1220

Nasıl Atıf Yapılır

VERİ MADENCİLİĞİ YAKLAŞIMI İLE TELEKOMUNİKASYON SEKTÖRÜNDE ARIZA GİDERME ANALİZİ. (2020). Business & Management Studies: An International Journal, 8(1), 1026-1043. https://doi.org/10.15295/bmij.v8i1.1220

Öz

Telekomünikasyon operatörleri tarafından sunulan hizmetlerde problem veya arıza ile karşılaşılmasının yanı sıra meydana gelecek bu tür olumsuzlukların giderilememesi, müşteri güvenini azaltmakta ve sonuç olarak gelir kaybına yol açmaktadır. Veri madenciliği, telekomünikasyon endüstrisinde mevcut verilere ilişkin analizler sayesinde geliştirilmiş bilgiyi sağlayabilmektedir. Bu çalışmada, Türkiye’de telekomünikasyon sektöründe yer alan öncü bir firmanın mobil ağlarında sorun giderme süreçlerine veri madenciliği süreci uygulanmıştır. Bu kapsamda Mart-Mayıs 2019 tarih aralığında elde edilen 4032 veri ön işleme süreci sonucunda 3748 örneklem olarak analize dahil edilmiştir. Arıza kaydı sürecinde kayıt altına alınan Arıza Merkezi, İş Emri, Ekip Numarası, Hizmet Türü, Hizmet Süresi, Şikâyet Türü ve Sonuç verilerinden oluşan 7 farklı değişken incelenmiştir. Elde edilen sonuçlara göre J48, PART ve Multilayer Perceptron sınıflandırıcılarının veri kümesinde daha iyi performans gösterdiği görülmüştür. Süreçlerin etkin bir şekilde kontrol etmesini sağlamada arıza giderme analizlerinde yol gösterici bir çalışma olması bakımından önem arz etmektedir.

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Referanslar

  1. Acker, O., Blockus, A., & Pötscher, F. (2013). Benefiting from big data: A new approach for the telecom industry. Strategy&, Analysis Report.
  2. Aliu, O. G., Imran, A., Imran, M. A., & Evans, B. (2012). A survey of self organisation in future cellular networks. IEEE Communications Surveys & Tutorials, 15(1), 336-361.
  3. Akçetin, E., & Çelik, U. (2014). İstenmeyen elektronik posta (spam) tespitinde karar ağacı algoritmalarının performans kıyaslaması. Journal of Internet Applications & Management/İnternet Uygulamaları ve Yönetimi Dergisi, 5(2).
  4. Albayrak, A. S., & YILMAZ, Ö. G. Ş. K. (2009). Veri madenciliği: Karar ağaci algoritmalari ve İMKB verileri üzerine bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1), 31-52.
  5. Apté, C., & Weiss, S. (1997). Data mining with decision trees and decision rules. Future generation computer systems, 13(2-3), 197-210.
  6. Aynekin, G. (2006). İnternet içerik madenciliğinde yapay sinir ağları ve bir uygulama (Master's thesis, Uludağ Üniversitesi).
  7. Barco, R., Guerrero, R., Hylander, G., Nielsen, L., Partanen, M., & Patel, S. (2002, September). Automated troubleshooting of mobile networks using bayesian networks. In Proc. IASTED International Conference on Communication Systems and Networks (CSN02), Malaga, Spain (p. 105110).
  8. Barco, R., Wille, V., & Díez, L. (2005). System for automated diagnosis in cellular networks based on performance indicators. European Transactions on Telecommunications, 16(5), 399-409.
  9. Bozkır, A. S. (2009). OLAP ve veri madenciliği teknolojilerinden yararlanılarak web tabanlı bir karar destek sisteminin gerçekleştirilmesi. Yayımlanmış Yüksek Lisans Tezi. Hacettepe Üniversitesi, Ankara.
  10. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  11. Calyam, P., Dovrolis, C., Jörgenson, L., Kettimuthu, R., Tierney, B., & Zurawski, J. (2013). Monitoring and troubleshooting multi-domain networks using measurement federations [guest editorial]. IEEE Communications Magazine, 51(11), 53-54.
  12. Cao, H., Dong, W. S., Liu, L. S., Ma, C. Y., Qian, W. H., Shi, J. W., ... & Cohen, D. (2014). SoLoMo analytics for telco Big Data monetization. IBM Journal of Research and Development, 58(5/6), 9-1.
  13. Chen, R., Ji, W., Duan, S., Ling, Q., & Li, F. (2017, August). A novel method to analyze logs generated by wireless telecommunication systems. In 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM) (pp. 235-238). IEEE.
  14. Cohen, W. W. (1995). Fast effective rule induction. In Machine learning proceedings 1995 (pp. 115-123). Morgan Kaufmann.
  15. Csikor, L., & Pezaros, D. P. (2017, December). End-Host driven troubleshooting architecture for software-defined networking. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1-7). IEEE.
  16. de Santana, F. B., Neto, W. B., & Poppi, R. J. (2019). Random forest as one-class classifier and infrared spectroscopy for food adulteration detection. Food chemistry, 293, 323-332.
  17. Devasena, C. L., Sumathi, T., Gomathi, V. V., & Hemalatha, M. (2011). Effectiveness evaluation of rule based classifiers for the classification of iris data set. Bonfring International Journal of Man Machine Interface, 1(Special Issue Inaugural Special Issue), 05-09.
  18. Diaz-Aviles, E., Pinelli, F., Lynch, K., Nabi, Z., Gkoufas, Y., Bouillet, E., ... & Salzwedel, J. (2015, October). Towards real-time customer experience prediction for telecommunication operators. In 2015 IEEE International Conference on Big Data (Big Data) (pp. 1063-1072). IEEE.
  19. Din, S., Ghayvat, H., Paul, A., Ahmad, A., Rathore, M. M., & Shafi, I. (2015, December). An architecture to analyze big data in the internet of things. In 2015 9th International Conference on Sensing Technology (ICST) (pp. 677-682). IEEE.
  20. Dunham, M. H. (2006). Data mining: Introductory and advanced topics. Pearson Education India.
  21. Fayyad, U. M. (1994, October). Branching on attribute values in decision tree generation. In AAAI (pp. 601-606).
  22. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine learning, 29(2-3), 131-163.
  23. Furletti, B., Gabrielli, L., Renso, C., & Rinzivillo, S. (2013, October). Analysis of GSM calls data for understanding user mobility behavior. In 2013 IEEE International Conference on Big Data (pp. 550-555). IEEE.
  24. Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300.
  25. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explor. Newsl., 11(1), 10-18. doi: 10.1145/1656274.1656278.
  26. Han, J., Kamber, M., & Pei, J. (2006). Data mining: concepts and techniques. 2001. San Francisco: Morgan Kauffman.
  27. Hilden, J. (1984). Statistical diagnosis based on conditional independence does not require it. Computers in biology and medicine, 14(4), 429-435.
  28. Jakobson, G., & Weissman, M. (1993). Alarm correlation. IEEE network, 7(6), 52-59.
  29. Kohavi, R. (1995, August). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai (Vol. 14, No. 2, pp. 1137-1145).
  30. Korting, T. S. (2006). C4. 5 algorithm and multivariate decision trees, image processing division. National Institute for Space Research–INPE, SP, Brazil.
  31. Laiho, J., Kylvaja, M., & Hoglund, A. (2002, May). Utilization of advanced analysis methods in UMTS networks. In Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No. 02CH37367) (Vol. 2, pp. 726-730). IEEE.
  32. Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining (Vol. 4). John Wiley & Sons.
  33. Li, H., Yang, D., Yang, L., & Lin, X. (2016, October). Supervised massive data analysis for telecommunication customer churn prediction. In 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom) (pp. 163-169). IEEE.
  34. Lilik, F., Simonyi, P., & Kóczy, L. T. (2013). Computational intelligence in performance evaluation and fault prognosis in telecommunication access networks. Czasopismo Techniczne.
  35. MathWorks, Inc. (2017). MATLAB 2017b.
  36. Melero, J., Halonen, T., & Romero, J. (2003). GSM, GPRS and edge performance: evolution towards 3G/UMTS. The British Library, TJ International.
  37. Mishra, A. R. (2004). Fundamentals of cellular network planning and optimisation. Evolution to. Wiley-Interscience.
  38. Network, S. O. (2009). NEC's proposals for next-generation radio network management. NEC Corporation Technical white paper.
  39. Pirinen, P. (2014, November). A brief overview of 5G research activities. In 1st International Conference on 5G for Ubiquitous Connectivity (pp. 17-22). IEEE.
  40. Rajput, A., Aharwal, RP, Dubey, M., Saxena, SP ve Raghuvanshi, M. (2011). E-yönetişim verileri için J48 ve JRIP kuralları. Uluslararası Bilgisayar Bilimi ve Güvenliği Dergisi (IJCSS) , 5 (2), 201.
  41. Rokach, L.& M. Oded (2008), Data Mining With Decision Trees, World Scientific, New Jersey.
  42. Rygielski, C., Wang, J.-C. & Yen, D. C.. (2002). Data mining techniques for customer relationship management. Technology in Society, 24 (4), 483-502.
  43. Saafein, O., & Shaykhian, G. A. (2014). Factors affecting virtual team performance in telecommunication support environment. Telematics and Informatics, 31(3), 459-462.
  44. Savaş, S., Topaloğlu N., & Yılmaz, M. (2012). Veri madenciliği ve türkiye’deki uygulama örnekleri. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, sayı 21, s. 1-23.
  45. Shahzad, W., Asad, S., & Khan, M. A. (2013). Feature subset selection using association rule mining and JRip classifier. International Journal of Physical Sciences, 8(18), 885-896.
  46. Singh, S., Liu, Y., Ding, W., & Li, Z. (2016, June). Evaluation of data mining tools for Telecommunication Monitoring Data using design of experiment. IEEE International Congress on Big Data (BigData Congress) (pp. 283-290). IEEE.
  47. Singh, S., Liu, Y., Ding, W., & Li, Z. (2016). Empirical evaluation of big data analytics using design of experiment: case studies on telecommunication data. Services Transactions on Big Data, 3(2).
  48. Škaljo, N., Begović, A., Turajić, E., & Behlilović, N. (2018). On ability of troubleshooting by observing some physical layer parameters of xdsl transceivers. International Journal of Electronics and Telecommunications, 64.
  49. Skračić, K., & Bodrušić, I. (2017, May). A Big Data solution for troubleshooting mobile network performance problems. 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 472-477). IEEE.
  50. Steinhauer, H. J., Helldin, T., Karlsson, A., & Mathiason, G. (2017, November). Topic modeling for situation understanding in telecommunication networks. 27th International Telecommunication Networks and Applications Conference (ITNAC) (pp. 1-6). IEEE.
  51. Sundsøy, P., Bjelland, J., Reme, B. A., Iqbal, A. M., & Jahani, E. (2016, January). Deep learning applied to mobile phone data for individual income classification. International Conference on Artificial Intelligence: Technologies and Applications. Atlantis Press.
  52. Theodorescu, R. (1968). Good, I. J.: The Estimation of Probabilities. An essay on modern Bayesian Methods. Research Monograph No. 30, The M. I. T. Press Cambridge 1965. 109 S., 7 Tab., 115 Lit. Biometrische Zeitschrift, 10(1), 87-87.
  53. Tomkos, I., Kachris, C., Khodashenas, P. S., & Soldatos, J. K. (2015, July). Optical networking solutions and technologies in the big data era. 17th International Conference on Transparent Optical Networks (ICTON) (pp. 1-1). IEEE.
  54. Toril, M., Pedraza, S., Ferrer, R., & Wille, V. (2002, May). Optimization of signal level thresholds in mobile networks. In Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002. (Vol. 4, pp. 1655-1659). IEEE.
  55. Telekomünikasyon hizmetleri - İstatistikler ve Gerçekler, 2018. https://www.statista.com/topics/2665/telecommunication-services/, erişim tarihi: 11.12.2018
  56. Tukey, J. W. (1962). The future of data analysis. The annals of mathematical statistics, 33(1), 1-67.
  57. Wille, V., Kuurne, A., Burden, S., Dunn, G., & Barco, R. (2002, September). Simulations and trial results for mobile measurement based frequency planning in GERAN networks. In Proceedings IEEE 56th Vehicular Technology Conference (Vol. 1, pp. 625-628). IEEE.

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