Çevik olgunluk modeli ve çeviklik değerlendirme aracı

TÜBİTAK EEEAG Proje01.04.2016The main purpose of the research project was to develop an agility maturity model and a toolbox in order to carry out agile software development processes compatible with manifesto for agile software development in software organizations in an effective and integrated way. “Agility maturity model” is defined as “agility assessment reference model” in such a way that the properties its name provides remain the same. In the scope of the project, the studies conducted are as follows: a) Development of AgilityMod agility assessment reference model: For the development of the model, the meta-structure of ISO 15504 process assessment reference models is taken as basis. Instead of process structure and practices of ISO 15504, new aspects and special practices are defined. Therefore, the model is suitable for agile process architecture and includes all components which are necessary for any assessment model. b) Development of assessment software: In order to facilitate and popularize the AgilityMod, a software is developed. c) Validation of the model in software organizations: AgilityMod is applied with the case studies in 9 different organizations and positive feedbacks are gathered. d) Introduction of the model in national and international conferences: The studies conducted during the project were published as 3 international conferences papers and presented in an educational workshop of a national conference. Additionally, 1 of the studies is accepted to be published in an international journal in 2016 and 2 of the studies are accepted to be presented in two international conferences

The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications

The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data