Supporting Telecommunication Alarm Management System with Trouble Ticket Prediction

Fault alarm data emanated from heterogeneous telecommunication network services and infrastructures are exploding with network expansions. Managing and tracking the alarms with Trouble Tickets using manual or expert rule- based methods has become challenging due to increase in the complexity of Alarm Management Systems and demand for deployment of highly trained experts. As the size and complexity of networks hike immensely, identifying semantically identical alarms, generated from heterogeneous network elements from diverse vendors, with data-driven methodologies has become imperative to enhance efficiency. In this paper, a data-driven Trouble Ticket prediction models are proposed to leverage Alarm Management Systems. To improve performance, feature extraction, using a sliding time-window and feature engineering, from related history alarm streams is also introduced. The models were trained and validated with a data-set provided by the largest telecommunication provider in Italy. The experimental results showed the promising efficacy of the proposed approach in suppressing false positive alarms with Trouble Ticket prediction

Syringa Networks v. Idaho Department of Administration Clerk\u27s Record v. 1 Dckt. 38735

https://digitalcommons.law.uidaho.edu/idaho_supreme_court_record_briefs/1519/thumbnail.jp

DECISION MAKING SUPPORT THROUGH A KNOWLEDGE MANAGEMENT FRAMEWORK FOR COMPLEX IT SYSTEMS DEVELOPMENT PROJECTS IN THE KINGDOM OF SAUDI ARABIA

Recent research reveals a narrow, rational model of problem- solving and decision-making in complex IT systems development projects. This creates problems that are identified in the thesis. The aim of this study is to develop a novel decision-making framework to support the decision-making process of managers of complex IT systems development projects by focusing on knowledge management frameworks. The objectives for the research were determined through a critical review of the existing research on decision-making in IT projects, primarily to discover how project managers’ decision-making can be supported through project-specific knowledge management. A qualitative research approach was then designed to investigate the phenomenon in its context by conducting in-depth semi-structured interviews. This study used qualitative data, through expert participants’ observations and opinions on IT systems development, particularly by understanding project management issues. The expert participants expressed their experiences through in-depth interviews. The collected data was then analysed using the thematic analysis technique and the findings were used to develop the IT Systems Development Decision-Making Support Framework. The Framework was then validated through focus group interviews. The main contribution of this research is based on the application of knowledge creation and knowledge management theories to decision-making frameworks for IT systems projects through the IT Systems Development Decision-Making Support Framework. The Framework is expected to enable decision evaluation and project-specific knowledge generation and sharing in IT systems development projects. This is vital for the type of contextual knowledge required for project-specific knowledge creation and management. Since IT systems development projects tend to be unique and their development process is complex, it is contended that an effective novel approach for modelling the expert decision-making process and assessing the defined model through project-specific knowledge activities is essential. This approach should help to deal with high level of complexity that is normally found in IT systems development projects

Design and implementation for automated network troubleshooting using data mining

The efficient and effective monitoring of mobile networks is vital given the number of users who rely on such networks and the importance of those networks. The purpose of this paper is to present a monitoring scheme for mobile networks based on the use of rules and decision tree data mining classifiers to upgrade fault detection and handling. The goal is to have optimisation rules that improve anomaly detection. In addition, a monitoring scheme that relies on Bayesian classifiers was also implemented for the purpose of fault isolation and localisation. The data mining techniques described in this paper are intended to allow a system to be trained to actually learn network fault rules. The results of the tests that were conducted allowed for the conclusion that the rules were highly effective to improve network troubleshooting.Comment: 19 pages, 7 figures, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.3, May 201