The planning work prior to implementing an SOA migration project is very important for its success. Up to now, most of this kind of work has been manual work. An SOA migration planning approach based on intelligent information processing methods is addressed to semi-automate the manual work. This thesis will investigate the principle research question: 'How can we obtain SOA migration planning schemas (semi-) automatically instead of by traditional manual work in order to determine if legacy software systems should be migrated to SOA computation environment?'. The controlled experiment research method has been adopted for directing research throughout the whole thesis. Data mining methods are used to analyse SOA migration source and migration targets. The mined information will be the supplementation of traditional analysis results. Text similarity measurement methods are used to measure the matching relationship between migration sources and migration targets. It implements the quantitative analysis of matching relationships instead of common qualitative analysis. Concretely, an association rule and sequence pattern mining algorithms are proposed to analyse legacy assets and domain logics for establishing a Service model and a Component model. These two algorithms can mine all motifs with any min-support number without assuming any ordering. It is better than the existing algorithms for establishing Service models and Component models in SOA migration situations. Two matching strategies based on keyword level and superficial semantic levels are described, which can calculate the degree of similarity between legacy components and domain services effectively. Two decision-making methods based on similarity matrix and hybrid information are investigated, which are for creating SOA migration planning schemas. Finally a simple evaluation method is depicted. Two case studies on migrating e-learning legacy systems to SOA have been explored. The results show the proposed approach is encouraging and applicable. Therefore, the SOA migration planning schemas can be created semi-automatically instead of by traditional manual work by using data mining and text similarity measurement methods.

Service-Oriented Architecture (SOA) provides a standards-based conceptual framework for flexible and adaptive systems and has become widely used in the recent years because of it. The number of legacy systems has already been migrated to this platform. As there are still many systems under consideration of such migration, we found it relevant to share the existing experience of SOA migration and highlight challenges that companies meet while adopting SOA. As not all of these migrations were successful, we also look into factors that have influence on the success of SOA projects. The research is based on two methods: a literature review and a survey. The results of the thesis include identification and quantitative analysis of the challenges and success factors of SOA projects. We also compare the survey results for different samples (based on the company industry, work area, size, and respondents experience with SOA and respondents job positions). In total, 13 SOA challenges and 18 SOA success factors were identified, analyzed and discussed in this thesis. Based on the survey results, there are three SOA challenges received the highest importance scores: “Communicating SOA Vision”, “Focus on business perspective, and not only IT perspective” and “SOA Governance”. The highest scored SOA success factor is “Business Process of Company”. While comparing different samples of the survey results, the most obvious differences are identified between the results received from people with development related job positions and people with business related job positions, and the results from companies of different sizes.