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GDMS A Geospatial Data Mining System for Abnormal Event Detection and Visualization


Scalable and Secure Big Data I
Abstract


Mobile devices have generated massive textual data with geographical locations. These data are applied to the topic discovery, event detection and user behavior analysis in enterprise systems. Therefore, geospatial data mining has become a very important and challenging research topic in such systems. In this paper, we develop a geospatial data mining system called GDMS to support the retrieval and analysis of textual data with geographical locations. The system contains three components: data collection, data analysis and data visualization. First, a large number of geospatical data are collected from our implemented mobile APP that is used by community residents. Residents can use the APP to upload abnormal events by text descriptions with geographical locations. All these events are processed and stored in a server. In the data analysis component, we focus on the problem of finding textual topics of clusters containing text descriptions with geographical locations. The key is how to combine clustering techniques with topic-retrieval models to integrate both geo-location information and text information. We investigated methods that combine clustering methods with the knowledge graph to discover topics of clusters of documents with geo-locations. Finally, we demonstrate an effective visualization tool that shows detected textual topics on the map in our mobile APP that is used by government staffs.

KeyWords
geospatial data mining, knowledge graph, topic detection, visualization



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