1 Department of Computer Science, The Faculty of Engineering and Science, Aalborg University, VBN2 Database and Programming Technologies, The Faculty of Engineering and Science, Aalborg University, VBN3 Daisy - Center for Data-intensive Systems, The Faculty of Engineering and Science, Aalborg University, VBN4 The Faculty of Engineering and Science, Aalborg University, VBN
Today, airport baggage handling is far from perfect. Baggage goes on the wrong flights, is left behind, or gets lost, which costs a lot of money for the airlines, as well as frustration for the passengers. To remedy the situation, we present a data warehouse (DW) solution for storing and analyzing spatio-temporal Radio Frequency Identification (RFID) baggage tracking data. Analysis of this data can yield interesting results on baggage flow, the causes of baggage mishandling, and the parties responsible for the mishandling(airline, airport, handler,...), which can ultimately lead to improved baggage handling quality. The paper presents a carefully designed data warehouse (DW), with a relational schema sitting underneath a multidimensional data cube, that can handle the many complexities in the data. The paper also discusses the Extract-Transform-Load (ETL) flow that loads the data warehouse with the appropriate tracking data from the data sources. The presented concepts are generalizable to other types of multi-site indoor tracking systems based on Bluetooth and RFID. The system has been tested with large amount of real-world RFID-based baggage tracking data from a major industry initiative. The developed solution is shown to both reveal interesting insights as well as being several orders of magnitude faster than computing the results directly on the data sources.
Ieee 14th International Conference on Mobile Data Management, 2013, p. 283-292
RFID; data warehouse; data cube; data analysis; baggage tracking; moving objects; indoor tracking;
Main Research Area:
the 14th IEEE International Conference on Mobile Data ManagementInternational Conference on Mobile Data Management, 2013