Contributions to Self-Organizing Networks and Network Measurement Data Management

Kasper Apajalahti

Research output: ThesisDoctoral ThesisCollection of Articles


In the future, the mobile network infrastructure needs to facilitate wireless communication to automate industry processes in many vertical domains. The heterogeneity of domains and use cases need to be addressed by various traffic service types that require new technologies. The management architecture of the upcoming 5G networks should cover flexible cross-platform optimization (both technology and administrative domains) and operator business objectives. The new management aspects combined with the increasing complexity of the mobile network infrastructure denote the necessity of the adaptive automation of operability and management. Along with the 4G, the Self-Organizing Networks (SON) paradigm has been designed and utilized to automate some network management use cases. The challenge in the future network management is the interplay of cross-platform management functionalities in complex 5G networks. Some of the objectives in the network management for operators in deciding the right context-specific solutions are: 1) comparing similar cross-platform SON functions and their configurations, 2) providing linkages of metrics across platforms, 3) providing graphical user interfaces to understand the decisions and actions of autonomic SON functions, and 4) automating the process of modelling SON functions and their metadata. This thesis is conducted by designing, implementing, and evaluating frameworks, models, and methods, that address the aforementioned challenges. The research follows the principles of the design science methodology. User interface functionalities with faceted browsing activities are designed in order to provide flexible information exploration for the user. The other user interface design offers an interactive SON function discovery mechanism for a prototype SON service system and the other provides an ontology-based visualization of the functionality of an individual SON function. The thesis presents semantic models for reasoning-based SON function discovery and composition mechanism and for defining metric dependencies. Statistical methods are developed for mining time series-based event patterns as context-specific metadata for SON functions and for matching network metrics across heterogeneous datasets with a correlation-based method. All the contributions are reflected against the related work and discussed from the viewpoint of practical benefits for network management. The contributions are novel in view of adapting methods from other research areas to the SON and network measurement data management.
Translated title of the contributionKontribuutioita itseorganisoituviin verkkoihin ja verkkojen mittaustietojen hallintaan
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
  • Hyvönen, Eero, Supervising Professor
  • Räisänen, Vilho, Thesis Advisor, External person
Print ISBNs978-952-60-8776-4
Electronic ISBNs978-952-60-8777-1
Publication statusPublished - 2019
MoE publication typeG5 Doctoral dissertation (article)


  • self-organizing networks
  • network measuring
  • data management


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