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Sensors 2016, 16(12), 2194; doi:10.3390/s16122194

Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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Academic Editor: Yike Guo
Received: 16 September 2016 / Revised: 5 December 2016 / Accepted: 12 December 2016 / Published: 20 December 2016
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
View Full-Text   |   Download PDF [13353 KB, 21 December 2016; original version 20 December 2016]   |  

Abstract

Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users’ spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring challenges to achieve such a task. Different from the previous work on social behavior analysis, in this paper, we present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to facilitate the analysis and visualization of social check-in data. We focus on three major components of user check-in data: location, activity, and profile. Visual fingerprints for location, activity, and profile are designed to intuitively represent the high-dimensional attributes. To visually mine and demonstrate the behavior features, we integrate WorldMapper and Voronoi Treemap into our glyph-like designs. Such visual fingerprint designs offer us the opportunity to summarize the interesting features and patterns from different check-in locations, activities and users (groups). We demonstrate the effectiveness and usability of our system by conducting extensive case studies on real check-in data collected from a popular microblogging service. Interesting findings are reported and discussed at last. View Full-Text
Keywords: visual mining; big data analysis; spatial and temporal behaviors; social media; Internet of things visual mining; big data analysis; spatial and temporal behaviors; social media; Internet of things
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pu, J.; Teng, Z.; Gong, R.; Wen, C.; Xu, Y. Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media. Sensors 2016, 16, 2194.

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