About BigData@BTH

Data will be generated at an ever-increasing rate for the foreseeable future. Added value and cost savings can be obtained by analyzing big data streams. The analysis of large data sets requires scalable and high-performance computer systems. In order to stay competitive and to reduce consumption of energy and other resources, the next generation systems for scalable big data analytics need to be more resource-efficient. The research profile, Scalable resource-efficient systems for big data analytics, combines existing expertise in machine learning, data mining, and computer engineering to create new knowledge in the area of scalable resource-efficient systems for big data analytics. The value of the new knowledge will be demonstrated and evaluated in two application areas (decision support systems and image processing).

The needs and interests of our 9 industrial partners are grouped into industrial challenges. Based on these challenges and in cooperation with our partners we have defined initial sub-projects grouped into four research themes:

Research theme A: Big data analytics for decision support
Research theme B: Big data analytics for image processing
Research theme C: Core technologies (machine learning)
Research theme D: Foundations and enabling technologies

This research profile is in the center of the university’s vision to be a globally attractive knowledge community within applied information technology and innovation for sustainable growth. The research group currently includes four full professors, two associate professors, four assistant professors, and a number of Ph.D. students. The mix of competences gives unique possibilities to develop new knowledge in the profile area. The profile includes a well-defined career advancement program and a visiting researcher program.

subprojects