ANALYTICS ON INDOOR MOVING OBJECTS WITH APPLICATIONS IN AIRPORT BAGGAGE TRACKING IN APPLICATIONS ON INDOOR MOVING OBJECTS WITH ANALYTICS ANALYTICS ON INDOOR MOVING OBJECTS WITH APPLICATIONS IN AIRPORT BAGGAGE TRACKING BY TANVIR AHMED DISSERTATION SUBMITTED 2016 TANVIR AHMED TANVIR IT BI D C Analytics on Indoor Moving Objects with Applications in Airport Baggage Tracking Ph.D. Dissertation Tanvir Ahmed Dissertation submitted March, 2016 A thesis submitted to the Faculty of Engineering and Science at Aalborg Uni- versity (AAU) and the Faculty of Engineering at Université Libre De Brux- elles (ULB), in partial fulfillment of the requirements within the scope of the IT4BI-DC programme for the joint Ph.D. degree in computer science. The thesis is not submitted to any other organization at the same time. Dissertation submitted: March, 2016 PhD supervisor: Prof. Torben Bach Pedersen Aalborg University, Aalborg, Denmark Assistant PhD supervisor: Assoc. Prof. Toon Calders Université Libre de Bruxelles, Brussels, Belgium PhD committee: Associate Professor Kristian Torp (chairman) Aalborg University, Denmark Associate Professor Peer Christian Kröger Ludwig-Maximilians-Universität München, Germany Associate Professor Alberto Abello Universitat Politécnica de Catalunya, Spain PhD Series: Faculty of Engineering and Science, Aalborg University ISSN (online): 2246-1248 ISBN (online): 978-87-7112-529-0 Published by: Aalborg University Press Skjernvej 4A, 2nd floor DK – 9220 Aalborg Ø Phone: +45 99407140 [email protected] forlag.aau.dk © Copyright by Tanvir Ahmed. Author has obtained the right to include the pub- lished and accepted articles in the thesis, with a condition that they are cited, DOI pointers and/or copyright/credits are placed prominently in the references. Printed in Denmark by Rosendahls, 2016 Abstract A large part of people’s lives are spent in indoor spaces such as office and university buildings, shopping malls, subway stations, airports, museums, community centers, etc. Such kind of spaces can be very large and paths inside the locations can be constrained and complex. Deployment of indoor tracking technologies like RFID, Bluetooth, and Wi-Fi can track people and object movements from one symbolic location to another within the indoor spaces. The resulting tracking data can be massive in volume. Analyzing these large volumes of tracking data can reveal interesting patterns that can provide opportunities for different types of location-based services, security, indoor navigation, identifying problems in the system, and finally service im- provements. In addition to the huge volume, the structure of the unprocessed raw tracking data is complex in nature and not directly suitable for further efficient analysis. It is essential to develop efficient data management tech- niques and perform different kinds of analysis to make the data beneficial to the end user. The Ph.D. study is sponsored by the BagTrack Project (http://daisy.aau.dk- /bagtrack). The main technological objective of this project is to build a global IT solution to significantly improve the worldwide aviation baggage handling quality. The Ph.D. study focuses on developing data management techniques for efficient and effective analysis of RFID-based symbolic indoor tracking data, especially for the baggage tracking scenario. First, the thesis describes a 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. The thesis presents the ETL flow that loads the data warehouse with the appropriate tracking data from the data sources. Second, the thesis presents 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. Third, the thesis presents an online risk prediction technique for indoor moving objects. The target is to develop a risk prediction system that can predict the risk of an object in real-time during its operation so that the object can be saved from being mishandled. Fourth, the thesis presents iii two graph-based models for constrained and semi-constrained indoor move- ments, respectively. These 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. Fifth, the thesis presents an efficient indexing technique, called the DLT-Index, for efficiently processing dense location queries as well as point and interval queries. The outcome of the thesis can contribute to the avi- ation industry for efficiently processing different analytical queries, finding problems in baggage management systems, and improving baggage handling quality. The developed data management techniques also contribute to the spatio-temporal data management and data mining field. Summary in Danish / Dansk Resumé Størstedelen af folks liv foregår indendørs, fx på kontorer, universiteter, ind- købs centre, undergrundsbaner stationer, lufthavne, museer, lokalcentre osv. Sådanne lokationer kan være meget store og det kan være svært at finde vej. Udvikling af indendørs sporings teknologier som fx RFID, Bluetooth og Wifi kan spore folk og objekter fra en symbolsk lokation til en anden inden i et indendørs område. Den resulterende sporingsdata kan være meget stor. Analyse af denne store mængde sporingsdata kan afsløre interessante data mønstre der kan give mulighed for forskellige typer of lokationsbestemte servicer, sikkerhed, navigation, problem identificering og service forbedring. Den store data mængde er ikke det eneste problem, strukturen af det upro- cesseret sporingsdata er kompleks af natur og den er ikke direkte brugbar til effektiv analyse. Det er derfor essentielt at udvikle effektiv datahåndter- ings teknikker og udføre data analyse for at gøre dette data brugbart for slutbrugeren. Dette Ph.d. studie er sponseret af BagTrack projektet (http://daisy.aau.dk- /bagtrack). Det primære tekniske mål af dette projekt er at bygge en global IT løsning der skal signifikant forbedre kvaliteten af luftfart bagage hånd- tering. Ph.d. studiet fokuserer på udvikling af forskellige data håndterings teknikker for effektiv analyse af RFID baseret symbolsk indendørs sporings- data, især på baggage håndtering. For det første så designer Ph.d. afhandling en data warehouse løsning baseret på et relationelt schema, oven på dette er der bygget en multidimensional data kube. Denne kube kan håndtere mange af kompleksiteterne fra det massive RFID bagage sporings system. ETL pro- cessen indlæser sporingsdataet ind i data warehouset fra datakilderne. For det andet så udvikler dette Ph.d. studie en metodologi for at finde risiko faktorer i RFID bagage sporingsdata. Målet er at finde faktorer og interes- sante mønstre der er for ansvarlige for baggage fejlbehandling. For det tredje så bliver der i dette studie udviklet en online risiko forudsigelses teknik for objekter der bevæger sig i symbolske lokationer. Den udviklede teknik vil v skulle være i stand til at forudsige om et objekt er i fare i realtid under selve baggage håndteringen. For det fjerde så designes der to modeller baseret på grafer, en for begrænsninger og en anden for semi-begrænset indendørs bevægelser. Modellerne bliver brugt til afbilding af sporings registre til reg- istre der repræsentere ind- og udgangs tider for objekter i symbolske loka- tioner. Disse afbildinger registre bliver brugt til at finde lokationer med høj objekt tæthed. For det femte så designer denne afhandling en effektiv Indek- sering teknik, kaldet DLT-Index, til effektiv processering af lokationer med høj objekt tæthed så vel som punkt og interval forsørgelser. Denne afhan- dling giver effektiv processering af forskellige analytiske forsørgelse, finder problemer i bagage håndterings systemer, og forbedre bagage håndterings kvaliteten for luftfartsindustrien. De udviklede data håndterings teknikker og analyser bidrager til spatio-temporal data håndtering og data mining forsknings områderne. Acknowledgements First of all, I praise the almighty Allah for giving me strength to complete this thesis. I sincerely like to thank my honorable Ph.D. advisor, Professor Torben Bach Pedersen for giving me the opportunity to work with him. I must have to express that he is an amazing Ph.D. supervisor and I am extremely lucky to be one of his Ph.D. students. My words are powerless to express my grat- itude to him. His patient guidance, encouragement, carefulness, advice, and prompt replies to questions and queries have played the important key role in this thesis. He has always provided very concrete ideas and comments throughout my Ph.D. research. I am extremely grateful to him for his enor- mous support during my Ph.D. study. I would also like to express my sincere appreciation and my sincere grati- tude to my Ph.D. co-advisors Associate Professor Hua Lu and Associate Pro- fessor Toon Calders for guiding me and teaching me different technologies during my Ph.D. research. I am very grateful for their valuable contributions to the publications. Their reviews and comments on the articles were very constructive. I have to say that without their help this thesis would not be a reality. I would like to thank all my colleagues in the Computer Science depart- ment at Aalborg University especially the Database and Programming Tech- nologies group. They were always ready to help me whenever I was in need. They were very talent as well as very friendly, and I had really a great time in the group. Special thanks to Kim Ahlstrøm Jakobsen for helping me on writ- ing the Danish summary. Thanks also to administrative staff Helle Schroll and Helle Westmark for their valuable supports regarding administrative is- sues. I would also like to appreciate the prompt help of IT support staff Lon Nguyen. I also do not want to miss the opportunity to thank my colleague Asif Iqbal Baba to whom I spent a big part of my department life as well as social life during my Ph.D. I also would like to thank all my colleagues in the WIT research group at Université Libre De Bruxelles where I spent 10 months of my Ph.D.