Mining Massive RFID, Trajectory, and Traffic Data Sets
coauthor: Jae-Gil Lee, KAIST - Korea Advanced Institute of Science and Technology
coauthor: Hector Gonzalez, Department of Computer Science, University of Illinois at Urbana-Champaign
coauthor: Xiaolei Li, Department of Computer Science, University of Illinois at Urbana-Champaign
published: Sept. 26, 2008, recorded: August 2008, views: 5149
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
With the wide availability of satellite, RFID, GPS, sensor, wireless, and video technologies, moving-object data has been collected in massive scale and is becoming increasingly rich, complex, and ubiquitous. There is an imminent need for scalable and flexible data analysis over moving-object information; and thus mining moving-object data has become one of major challenges in data mining. There have been considerable research efforts on data mining for RFID, trajectory, and traffic data sets. However, there has been no systematic tutorial on knowledge discovery from such moving-object data sets. This tutorial presents a comprehensive, organized, and state-of-the-art survey on methodologies and algorithms on analyzing different kinds of moving-object data sets, with an emphasis on several important mining tasks: clustering, classification, outlier analysis, and multidimensional analysis. Besides a thorough survey of the recent research work on this topic, we also show how real-world applications can benefit from data mining of RFID, trajectory, and traffic data sets. The tutorial consists of three parts: (1) RFID data mining, (2) trajectory data mining, and (3) traffic data mining. In the first part, warehousing, cleaning, and flow mining for RFID data are explored. In the second part, pattern mining, clustering, classification, and outlier detection for trajectory data are explored. In the third part, route discovery, destination prediction, and hot-route or outlier detection for traffic data are explored. This tutorial is prepared for data mining, database, and machine learning researchers who are interested in moving-object data.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !