nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

Text Categorization for Deriving the Application Quality in Enterprises Using Ticketing Systems

verfasst von : Thomas Zinner, Florian Lemmerich, Susanna Schwarzmann, Matthias Hirth, Peter Karg, Andreas Hotho

Erschienen in: Big Data Analytics and Knowledge Discovery

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Today’s enterprise services and business applications are often centralized in a small number of data centers. Employees located at branches and side offices access the computing infrastructure via the internet using thin client architectures. The task to provide a good application quality to the employers using a multitude of different applications and access networks has thus become complex. Enterprises have to be able to identify resource bottlenecks and applications with a poor performance quickly to take appropriate countermeasures and enable a good application quality for their employees. Ticketing systems within an enterprise use large databases for collecting complaints and problems of the users over a long period of time and thus are an interesting starting point to identify performance problems. However, manual categorization of tickets comes with a high workload.

In this paper, we analyze in a case study the applicability of supervised learning algorithms for the automatic identification of relevant tickets, i.e., tickets indicating problematic applications. In that regard, we evaluate different classification algorithms using 12,000 manually annotated tickets accumulated in July 2013 at the ticketing system of a nation-wide operating enterprise. In addition to traditional machine learning metrics, we also analyze the performance of the different classifiers on business-relevant metrics.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel New Word Detection and Tagging on Chinese Twitter Stream

Nächstes Kapitel MultiSpot: Spotting Sentiments with Semantic Aware Multilevel Cascaded Analysis

https://github.com/lsinfo3/dataset_ticketing_system_Dawak_2015.

http://www.otrs.com/.

https://rapidminer.com.

Due to space constraints we do not report on initial experiments performed for parameter optimization.

Aha, D.: Lazy learning. Springer Science and Business Media, Heidelberg (1997)CrossRefMATH

Altintas, M., Tantug, A.C.: Machine learning based ticket classification in issue tracking systems. In: Proceedings of International Conference on Artificial Intelligence and Computer Science (AICS 2014) (2014)

Casas, P., Seufert, M., Egger, S., Schatz, R.: Quality of experience in remote virtual desktop services. In: Proceedings of the Workshop on Quality of Experience Centric Management (QCMAN), Ghent (2013)

Chandrinos, K.V., Androutsopoulos, I., Paliouras, G., Spyropoulos, C.D.: Automatic web rating: filtering obscene content on the web. In: Borbinha, J.L., Baker, T. (eds.) ECDL 2000. LNCS, vol. 1923, pp. 403–406. Springer, Heidelberg (2000) CrossRef

Chulani, S., Santhanam, P., Moore, D., Leszkowicz, B., Davidson, G.: Deriving a software quality view from customer satisfaction and service data. In: European Conference on Metrics and Measurement (2001)

Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefMATH

Diao, Y., Jamjoom, H., Loewenstern, D.: Rule-based problem classification in it service management. In: Proceedings of IEEE International Conference on Cloud Computing (CLOUD 2009), pp. 221–228. IEEE (2009)

Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: International Conference on Information and Knowledge Management, pp. 148–155. ACM (1998)

Good, I.J., Hacking, I., Jeffrey, R.C., Törnebohm, H.: The estimation of probabilities: an essay on modern bayesian methods. Synthese 16(2), 234–244 (1966)CrossRef

10.

Guzella, T.S., Caminhas, W.M.: A review of machine learning approaches to spam filtering. Expert Syst. Appl. 36(7), 10206–10222 (2009)CrossRef

11.

Hofmann, T., Schölkopf, B., Smola, A.J.: Kernel methods in machine learning. Ann. Stat. 36(3), 1171–1220 (2008)CrossRefMATH

12.

Hotho, A., Nürnberger, A., Paaß, G.: A brief survey of text mining. Ldv Forum 20, 19–62 (2005)

13.

Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Springer, Heidelberg (1998)

14.

Kreyss, J., Selvaggio, S., White, M., Zakharian, Z.: Text mining for a clear picture of defect reports: a praxis report. In: Proceedings of International Conference on Data Mining, Melbourne (2003)

15.

Le Callet, P., Möller, S., Perkis, A., et al.: Qualinet white paper on definitions of quality of experience. European Network on Quality of Experience in Multimedia Systems and Services (COST Action IC 1003) (2012)

16.

Medem, A., Akodjenou, M.-I., Teixeira, R.: Troubleminer: mining network trouble tickets. In: Proceedings of Symposium on Integrated Network Management, Long Island (2009)

17.

Mockus, A., Zhang, P., Li, P.L.: Predictors of customer perceived software quality. In: Proceedings of International Conference on Software Engineering, St. Louis (2005)

18.

Salton, G., Wong, A., Yang, C.-S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefMATH

19.

Schlosser, D., Staehle, B., Binzenhöfer, A., Boder, B.: Improving the qoe of citrix thin client users. In: Proceedings of International Conference on Communications, Cape Town (2010)

20.

Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRef

21.

Wei, X., Sailer, A., Mahindru, R., Kar, G.: Automatic structuring of it problem ticket data for enhanced problem resolution. In: 10th IFIP/IEEE International Symposium on IM 2007 Integrated Network Management, pp. 852–855. IEEE (2007)

Titel: Text Categorization for Deriving the Application Quality in Enterprises Using Ticketing Systems
verfasst von: Thomas Zinner
Florian Lemmerich
Susanna Schwarzmann
Matthias Hirth
Peter Karg
Andreas Hotho
Verlag: Springer International Publishing
Buch: Big Data Analytics and Knowledge Discovery
Print ISBN: 978-3-319-22728-3

Electronic ISBN: 978-3-319-22729-0

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-22729-0_25

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"