Massive Radio Frequency Identification (RFID) datasets are expected to become commonplace in supply-chain management systems. Warehousing and mining this data is an essential problem with great potential benefits for inventory management, object tracking, and product procurement processes. Since RFID tags can be used to identify each individual item, enormous amounts of location-tracking data are generated. Furthermore, RFID tags can record sensor information such as temperature or humidity. With such data, object movements can be modeled by movement graphs, where nodes correspond to locations, and edges record the history of item transitions between locations and sensor readings recorded during the transition. This chapter shows the benefits of the movement graph model in terms of compact representation, complete recording of spatio-temporal and item level information, and its role in facilitating multidimensional analysis. Compression power and efficiency in query processing are gained by organizing the model around the concept of gateway nodes, which serve as bridges connecting different regions of graph, and provide a natural partition of item trajectories. Multi-dimensional analysis is provided by a graph-based object movement data cube that is constructed by merging and collapsing nodes and edges according to an application-oriented topological structure.
|Original language||English (US)|
|Title of host publication||Intelligent Techniques for Warehousing and Mining Sensor Network Data|
|Number of pages||22|
|State||Published - 2009|
ASJC Scopus subject areas
- Social Sciences(all)