K
Kari Kolehmainen
Researcher at VTT Technical Research Centre of Finland
Publications - 13
Citations - 113
Kari Kolehmainen is an academic researcher from VTT Technical Research Centre of Finland. The author has contributed to research in topics: Extreme programming & Requirement. The author has an hindex of 4, co-authored 11 publications receiving 91 citations. Previous affiliations of Kari Kolehmainen include Nokia.
Papers
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Book ChapterDOI
Self-Adaptability of Agile Software Processes: A Case Study on Post-iteration Workshops
TL;DR: Empirical results show that with less than 4% effort it is possible to hold post-iteration workshops that significantly help to improve and optimize practices and enhance the learning and satisfaction of the project team.
Patent
Apparatus, method and computer program product for reducing power consumption based on relative importance
Mika Hongisto,Kari Kolehmainen +1 more
TL;DR: In this article, an apparatus, method and computer program product are provided for reducing power consumption of an electronic device by taking into consideration not only the load history of each run-time entity operating on the electronic device, but also the importance of those runtime entities.
Proceedings ArticleDOI
SWAMP: Smart Water Management Platform Overview and Security Challenges
Carlos Kamienski,João Henrique Kleinschmidt,Juha-Pekka Soininen,Kari Kolehmainen,Luca Roffia,M. C. Visoli,Rodrigo Filev Maia,Stenio Fernandes +7 more
TL;DR: Security challenges and technologies for the application of IoT in agriculture are discussed and it is indicated that one of the most relevant challenges to be handled in SWAMP project is dealing with the multitude of behaviors from IoT application.
Journal ArticleDOI
Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture
Franklin M. Ribeiro Junior,Reinaldo A. C. Bianchi,Ronaldo C. Prati,Kari Kolehmainen,Juha-Pekka Soininen,Carlos Kamienski +5 more
TL;DR: In this article , the authors proposed an approach to collect and store data in a fog-based smart agriculture environment and different data reduction methods were investigated; eight machine learning (ML) methods combined with run-length encoding, and eight combined with Huffman encoding.
Supporting requirements engineering in extreme programming: managing user stories
TL;DR: The objective of this paper is to examine the challenges involved in the requirements management in an XP project, and to study possibilities to integrate the support for user story management into single tool framework.