Tanvir Ahmed

A Data Warehouse Solution for Analyzing RFID-Based Baggage Tracking Data

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.

Capturing hotspots for constrained indoor movement

Finding the hotspots in large indoor spaces is very important for getting overloaded locations, security, crowd management, indoor navigation and guidance. The tracking data coming from indoor tracking are huge in volume and not readily available for finding hotspots. This paper presents a graph-based model for constrained indoor movement that can map the tracking records into mapping records which represent the entry and exit times of an object in a particular location. Then it discusses the hotspots extraction technique from the mapping records.

Mining Risk Factors in RFID Baggage Tracking Data

Airport baggage management is a significant part of the aviation industry. However, for several reasons every year a vast number of bags are mishandled (e.g., Left behind, send to wrong flights, gets lost, etc.,) which costs a lot of money to the aviation industry as well as creates inconvenience and frustration to the passengers. To remedy these problems we propose a detailed methodology for mining risk factors from Radio Frequency Identification (RFID) baggage tracking data. The factors should identify potential issues in the baggage management. However, the baggage tracking data are low level and not directly accessible for finding such factors. Moreover, baggage tracking data are highly imbalanced, for example, our experimental data, which is a large real-world data set from the Scandinavian countries, contains only 0.8% mishandled bags. This imbalance presents difficulties to most data mining techniques. The paper presents detailed steps for pre-processing the unprocessed raw tracking data for higher-level analysis and handling the imbalance problem. We fragment the data set based on a number of relevant factors and find the best classifier for each of them. The paper reports on a comprehensive experimental study with real RFID baggage tracking data and it shows that the proposed methodology results in a strong classifier, and can find interesting concrete patterns and reveal useful insights of the data.

Online Risk Prediction for Indoor Moving Objects

Technologies such as RFID and Bluetooth have received considerable attention for tracking indoor moving objects. In a time-critical indoor tracking scenario such as airport baggage handling, a bag has to move through a sequence of locations until it is loaded into the aircraft. Inefficiency or inaccuracy at any step can make the bag risky, i.e., the bag may be delayed at the airport or sent to a wrong airport. In this paper, we propose a novel probabilistic approach for predicting the risk of an indoor moving object in real-time. We propose a probabilistic flow graph (PFG) and an aggregated probabilistic flow graph (APFG) that capture the historical object transitions and the durations of the transitions. In the graphs, the probabilistic information is stored in a set of histograms. Then we use the flow graphs for obtaining a risk score of an online object and use it for predicting its riskiness. The paper reports a comprehensive experimental study with multiple synthetic data sets and a real baggage tracking data set. The experimental results show that the proposed method can identify the risky objects very accurately when they approach the bottleneck locations on their paths and can significantly reduce the operation cost.

Risk detection and prediction from indoor tracking data

Technologies such as RFID and Bluetooth have received considerable attention for tracking indoor moving objects. In a time-critical indoor tracking scenario such as airport baggage handling, a bag has to move through a sequence of locations until it is loaded into the aircraft. Inefficiency or inaccuracy at any step can make the bag risky, i.e., the bag may be delayed at the airport or sent to a wrong airport. In this paper, we discuss a risk detection and a risk prediction method for such kinds of indoor moving objects. We propose a data mining methodology for detecting risk factors from RFID baggage tracking data. The factors should identify potential issues in the baggage management. The paper presents the essential steps for pre-processing the unprocessed raw tracking data and discusses how to deal with the class imbalance problem present in the data set. Next, we propose an online risk prediction system for time constrained indoor moving objects, e.g., baggage in an airport. The target is to predict the risk of an object in real-time during its operation so that it can be saved before being mishandled. We build a probabilistic flow graph that captures object flow and transition times using least duration probability histograms, which in turn is used to obtain a risk score of an online object in risk prediction.

Finding dense locations in symbolic indoor tracking data: modeling, indexing, and processing

Finding the dense locations in large indoor spaces is very useful for many applications such as overloaded area detection, security control, crowd management, indoor navigation, and so on. Indoor tracking data can be enormous and are not immediately ready for finding dense locations. This paper presents two graph-based models for constrained and semi-constrained indoor movement, respectively, and then uses the models to map raw tracking records into mapping records that represent object entry and exit times in particular locations. Subsequently, an efficient indexing structure called Hierarchical Dense Location Time Index (HDLT-Index) is proposed for indexing the time intervals of the mapping table, along with index construction, query processing, and pruning techniques. The HDLT-Index supports very efficient aggregate point, interval, and duration queries as well as dense location queries. A comprehensive experimental study with both real and synthetic data shows that the proposed techniques are efficient and scalable and outperforms RDBMSs significantly.

Analytics on Indoor Moving Objects with Applications in Airport Baggage Tracking

A large part of peoples 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 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 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

An initial centroid selection method based on radial and angular coordinates for K-means algorithm

DLT

ANALYTICS ON INDOOR MOVING OBJECTS: A STUDY ON RFID BASED AIRPORT BAGGAGE HANDLING SYSTEM

-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.