Abstract:
Recent years have witnessed an enormous increase in moving object data originating from applications such as RFID enabled systems in supply chain operations and traffic monitoring applications in large road networks. Effective exploration and analysis of such data will have an important impact on areas such as business process optimization, city planning, and national security. In order to realize these benefits, we need to develop techniques to cope with the enormous size, complex spatiotemporal structure, and high noise levels of this data.

The goal of my research is to develop efficient methods to manage, warehouse, and mine moving object data.  In my talk, I will present two themes in this direction. The first, in the context of supply chain operations, is the development of a method to compress and warehouse very large RFID data sets, based on the following observations: (1) lossless data compression can be achieved by removing sensor generated redundancy, and exploiting group object movements, observed in many applications, (2) spatiotemporal characteristics of the data, can be preserved for OLAP analysis at multiple abstraction levels, by the usage of a hierarchical object identification schema. The second theme, in the context of traffic mining, deals with the problem of route recommendations on large road networks. The proposed method differs from previous approaches, in that it takes into consideration of driving patterns, mined from the data, to find routes that are not only fast but also favored by actual drivers.

Biography:
Hector Gonzalez finished his B.Sc. degree in Computer Science from Universidad Eafit, where he graduated first in class. He completed an M.B.A. at Harvard Business School, graduating with distinction in 1999. In 2002, Hector started his Ph.D. in computer science at the University of Illinois at Urbana-Champaign. He performs research on data mining, data warehousing and database systems, supervised by Professor Jiawei Han. His thesis, entitled "Mining Massive Moving Object Datasets: From RFID Data Flow Analysis to Traffic Mining" deals with warehousing, mining, and managing object tracking data from supply chain applications, to transportation engineering systems. His research, has resulted in publications at major conferences in database systems and data mining, such as VLDB, ICDE, CIKM, and SDM. One of his research papers, "Warehousing and Analysis of Massive RFID Data Sets", received the Best Student Paper award at ICDE'06.