Abstract
Due to the widespread usage of Internet of things devices in online-to-offline businesses, a huge volume of data from heterogeneous data sources are collected and transferred to the data processing components in online-to-offline systems. This leads to increased complexity in data storage and querying, especially for spatial–temporal data processing in online-to-offline systems. In this article, first, we design a multi-layer Internet of things database schema to meet the diverse requirements through fusing spatial data with texts, images, and videos transferred from the sensors of the Internet of things networks. The proposed multi-layer Internet of things database schema includes logical nodes, geography nodes, storage nodes, and application nodes. These data nodes cooperate with each other to facilitate the data storing, indexing, and querying. Second, a searching algorithm is designed based on pruning strategy. The complexity of the algorithm is also analyzed. Finally, the multi-layer Internet of things database schema and its application are illustrated in a smart city construction project in Shanghai, China, recommending available charging points to the customers who need to charge their electric energy–driven cars.
Highlights
Online to offline (O2O) is a business model, which tries to attract the customers from surfing online to the physical stores to make their transactions offline.[1]
The main contributions of this article are summarized as follows: 1. We proposed a multi-layer Internet of things (IoT) database schema to describe the heterogeneous information of O2O systems in a decoupled hierarchical way
The logical nodes act as coordinators during data query
Summary
Online to offline (O2O) is a business model, which tries to attract the customers from surfing online to the physical stores to make their transactions offline.[1] The usage of Internet of things (IoT) technologies in O2O can achieve more accurate results by targeting customers through analyzing their offline data. It can improve the loyalty of the consumers by providing more frequent interactions between consumers and business scenarios. It is important to propose methods to combine the IoT data with context data to support complicated O2O applications
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