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Welcome to my web page. My name is Tanvir Ahmed. I am working as a data scientist in a Danish company RadioAnalyzer Aps. I completed my PhD from the Database and Programming Technologies group (DPT) at the Department of Computer Science, Faculty of Engineering and Science, Aalborg University (AAU), Aalborg, Denmark. I was working at at the Center for Data-Intensive Systems (Daisy). I was working on the on the BagTrack project, during my PhD study. My PhD supervisor is Professor Torben Bach Pedersen and Co-Supervisor is Associate Professor Hua Lu. My PhD study started in January 2012. Before starting my PhD, I worked as an Assistant Professor in the Department of Computer Science at American International University - Bangladesh (AIUB), Banani, Dhaka, Bangladesh. I joined at AIUB as a lecturer in January 2008. I am also CISCO Certified Instructor of CISCO IT Essentials. I have completed my Master of Science in Computer Science (Specialization in Information and Database) from American International University-Bangladesh in December 2009. I have also completed my Bachelor of Science in Computer Science and Engineering (CSE) from AIUB in December 2007. I also worked as a part-time Database Consultant in an IT Company Infolink Bangladesh from May 2011 to December 2011.

Summary of my PhD thesis:

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 improvements. 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 techniques and perform different kinds of analysis to make the data beneficial to the end user.

The PhD study is sponsored by the BagTrack project. The main technological 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 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 two graph-based models for constrained and semi-constrained indoor movements, 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 aviation 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.

The full-text of my PhD thesis is available here.

Research Interest

Information and Database Management is my main field of interest.
My research interest includes: 
  • Information and Database Management
  • Multidimensional Database
  • Moving object database (specially on indoor scenario)
  • Spatio-temporal data analytics
  • Data Warehousing and OLAP
  • Social network data Analytics
  • Data Integration
  • Data Mining


  • Technical Program Committe Member: IEEE UMITS 2016 , April 25-29, 2016, Istanbul, Turkey
  • Technical Program Committe Member: IEEE UMITS 2016 , June 27-30, 2016, Messina, Italy
  • Reviewer: 12th IEEE eScience 2016 , October 23-27, 2016, Baltimore, Maryland, USA
  • Reviewer: 24th ACM SIGSPATIAL GIS 2016 , October 31 - November 3, 2016, San Francisco Bay Area, California, USA
  • Membership

  • I am a member of IEEE
  • I am also a member of IEB (Institute of Engineers, Bangladesh)