Abstract
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.
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.
Original language | English |
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Title of host publication | IEEE 14th International Conference on Mobile Data Management |
Number of pages | 10 |
Volume | 1 |
Publisher | IEEE Computer Society Press |
Publication date | Jun 2013 |
Pages | 283-292 |
ISBN (Print) | 978-1-4673-6068-5 |
ISBN (Electronic) | 978-0-7695-4973-6 |
DOIs | |
Publication status | Published - Jun 2013 |
Event | the 14th IEEE International Conference on Mobile Data Management - Milan, Italy Duration: 3 Jun 2013 → 6 Jun 2013 Conference number: 14 |
Conference
Conference | the 14th IEEE International Conference on Mobile Data Management |
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Number | 14 |
Country/Territory | Italy |
City | Milan |
Period | 03/06/2013 → 06/06/2013 |
Keywords
- RFID; data warehouse; data cube; data analysis; baggage tracking; moving objects; indoor tracking;