Massive amount of vehicle trajectory data is an important data source and has been widely used in many research fields. However, due to the huge volume and variety of application scenarios, it is still not easy to achieve the application-oriented and efficient retrieval of vehicle trajectory. The paper proposes a multi-granularity vehicle trajectory index method based on the concept lattice model. The method identifies the multi-granularity feature regions for application requirements to segment vehicle trajectory and employs the concept lattice model to maintain the complex relationships between vehicle trip and the feature regions, to realize the vehicle travel trajectory data retrieval for the multi-granularity feature regions. In this experiment, shanghai city is divided into 21 feature regions according to the county-level area of Shanghai, and the concept lattice index structure of vehicle trips is constructed. The availability and efficiency of the proposed method are verified by the comparative experimental results.
KEYWORDS: Mining, Data analysis, Computer engineering, Spatial analysis, Data storage, Data mining, Binary data, Algorithm development, Mobile devices, Logic
The popularization of 5G technology and the development of mobile network devices have given spatial textual dataset more dimensions, which means that the spatial text datasets recording geographical objects are given multiple source and attributes. Data mining on these datasets has become a meaningful work. Top-k spatial keyword query as a common research using spatial textual big data, after years of development, people have proposed a large number of index framework to achieve query. However, previous work often only focused on location and small amount of text, ignoring the associations between objects in spatial textual big data. In order to mine the knowledge contained in the dataset, we propose a Top-k Frequent spatial Keyword Query (TkFKQ) algorithm to index the frequent items in spatial textual dataset. In order to achieve this index, we design an index framework for knowledge mining of spatial textual data sets. The framework combines R-tree with concept lattice for TkFKQ. A large number of comparative experiments are carried out on the real data set to evaluate the method.
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