The project is sponsored by the . The main technology objective of this project is to build a global IT solution to significantly improve the worldwide aviation baggage handling quality. The PhD study focuses on developing different data management techniques for efficient and effective analysis of RFID-based symbolic indoor tracking data, especially for the baggage tracking scenario. First, the thesis carefully designed a data warehouse solution with a relational schema sitting underneath a multidimensional data cube, that can handle the many complexities in the massive non-traditional RFID baggage tracking data. It designs the ETL flow that loads the data warehouse with the appropriate tracking data from the data sources. Second, the thesis designs two graph-based models one for constrained and another for semi-constrained indoor movements. The models are used for mapping the tracking records into mapping records that represent the entry and exit times of an object at a symbolic location. The mapping records are then used for finding dense locations. Third, the thesis designs an efficient indexing technique called DLT-Index for efficient processing of dense locations queries as well as point and interval queries. Fourth, the study also develops a methodology for mining risk factors in RFID baggage tracking data. The aim is to find the factors and interesting patterns that are responsible for baggage mishandling. Finally, the study develops an online risk prediction technique for the objects moving in symbolic location. The developed technique should be able to predict an object in risk in real time during its operation. The outcome of the thesis can contribute to the aviation industry for efficiently processing different analytical queries, finding problems in the baggage management systems, and improving baggage handling quality. The resultant data management techniques and analytics will contribute to the spatio-temporal data management field. Information and Database Management is my main field of interest.
Mechanical properties of 3D printed objects
Fused Deposition modeling (FDM) has revolutionized the possibilities of 3D printing, which will allow absolutely new production routes (4. industrial revolution). We are interested in the mechanical properties of produced devices. The bachelor thesis designs and prints mechanical testing samples of thermoplastic materials. The goal is to determine and explain potential anisotropies of the mechanical behaviour in tensile testing or low cycling fatigue experiments. (Supervisor: Patrick Stender)
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