Smartphones and other sensor devices can generate large amounts of data in a short time. Even if their computing capacity and battery lifetime can be limited, they usually have good communication capabilities, so that they can take advantage of remote services. Large-scale data analysis offers methodology, which can be used to improve functionality of the applications and extend the user activity. This creates a need for an iterative data analysis system, which offers streaming processing, data flow management, and machine learning algorithms suitable for complex sensing data. My PhD work aims to design principles and practices for an iterative data analysis algorithms and workflow. As a case study, we are building a mobile application that measures context factors’ combined impact to energy consumption. Our approach will be useful for different types of cases, where it is important to understand complex data sources in real time.
|Title of host publication||Pervasive Computing and Communication Workshops (PerCom Workshops), 2015 IEEE International Conference|
|Number of pages||2|
|Publication date||23 Mar 2015|
|Publication status||Published - 23 Mar 2015|
|MoE publication type||A4 Article in conference proceedings|
|Event||IEEE International Conference on Pervasive Computing and Communications Workshops - St. Louis, MO, United States|
Duration: 23 Mar 2015 → 27 Mar 2015
Bibliographical noteRunner-up: Best PhD Forum Presentation Award.
Fields of Science
- 113 Computer and information sciences