International Trade Modelling Using Open Flow Networks: A Flow-Distance Based Analysis.

This paper models and analyzes international trade flows using open flow networks (OFNs) with the approaches of flow distances, which provide a novel perspective and effective tools for the study of international trade. We discuss the establishment of OFNs of international trade from two coupled viewpoints: the viewpoint of trading commodity flow and that of money flow. Based on the novel model with flow distance approaches, meaningful insights are gained. First, by introducing the concepts of trade trophic levels and niches, countries' roles and positions in the global supply chains (or value-added chains) can be evaluated quantitatively. We find that the distributions of trading "trophic levels" have the similar clustering pattern for different types of commodities, and summarize some regularities between money flow and commodity flow viewpoints. Second, we find that active and competitive countries trade a wide spectrum of products, while inactive and underdeveloped countries trade a limited variety of products. Besides, some abnormal countries import many types of goods, which the vast majority of countries do not need to import. Third, harmonic node centrality is proposed and we find the phenomenon of centrality stratification. All the results illustrate the usefulness of the model of OFNs with its network approaches for investigating international trade flows.

A framework for mining actionable navigation patterns from in-store RFID datasets via indoor mapping.

With the quick development of RFID technology and the decreasing prices of RFID devices, RFID is becoming widely used in various intelligent services. Especially in the retail application domain, RFID is increasingly adopted to capture the shopping tracks and behavior of in-store customers. To further enhance the potential of this promising application, in this paper, we propose a unified framework for RFID-based path analytics, which uses both in-store shopping paths and RFID-based purchasing data to mine actionable navigation patterns. Four modules of this framework are discussed, which are: (1) mapping from the physical space to the cyber space, (2) data preprocessing, (3) pattern mining and (4) knowledge understanding and utilization. In the data preprocessing module, the critical problem of how to capture the mainstream shopping path sequences while wiping out unnecessary redundant and repeated details is addressed in detail. To solve this problem, two types of redundant patterns, i.e., loop repeat pattern and palindrome-contained pattern are recognized and the corresponding processing algorithms are proposed. The experimental results show that the redundant pattern filtering functions are effective and scalable. Overall, this work builds a bridge between indoor positioning and advanced data mining technologies, and provides a feasible way to study customers' shopping behaviors via multi-source RFID data.