Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach
Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach
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DOI：
10.1109/tkde.2009.61
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发表时间：
2010-01-01
影响因子：
8.9
通讯作者：
Wu, Tianyi
中科院分区：
文献类型：
--
作者：
Gonzalez, Hector;Han, Jiawei;Wu, Tianyi
海量射频识别 (RFID) 数据集预计将在供应链管理系统中变得司空见惯。仓储和挖掘这些数据是一个重要问题，对库存管理、对象跟踪和产品采购流程具有巨大的潜在好处。由于 RFID 标签可用于识别每个单独的物品，因此会生成大量的位置跟踪数据。利用这些数据，可以通过运动图对对象运动进行建模，其中节点对应于位置，边记录位置之间项目转换的历史。在本研究中，我们开发了一个运动图模型作为 RFID 数据集的紧凑表示。由于时空以及物品信息可以与此类模型中的对象相关联，因此运动图本质上可以是巨大的、复杂的和多维的。我们表明，这样的图可以更好地围绕网关节点进行组织，网关节点充当连接运动图不同区域的桥梁。可以根据面向应用的拓扑结构通过合并和折叠节点和边来构造基于图的对象运动立方体。此外，我们提出了一种有效的立方算法，在这种拓扑结构的指导下，在分区的运动图上同时执行时空维度和项目维度的聚合。
Massive Radio Frequency Identification (RFID) data sets 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. 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. In this study, we develop a movement graph model as a compact representation of RFID data sets. Since spatiotemporal as well as item information can be associated with the objects in such a model, the movement graph can be huge, complex, and multidimensional in nature. We show that such a graph can be better organized around gateway nodes, which serve as bridges connecting different regions of the movement graph. A graph-based object movement cube can be constructed by merging and collapsing nodes and edges according to an application-oriented topological structure. Moreover, we propose an efficient cubing algorithm that performs simultaneous aggregation of both spatiotemporal and item dimensions on a partitioned movement graph, guided by such a topological structure.