In: Proceedings of the Web Conference (WWW 2019)
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In: IEEE Transactions on Knowledge and Data Engineering, 2018, p. 1–1
Histogram-based similarity has been widely adopted in many machine learning tasks. However, measuring histogram similarity is a challenging task for streaming histograms, where the elements of a histogram are observed one after the other in an online manner. The ever-growing cardinality of histogram elements over the data streams makes any similarity computation inefficient in that case. To...
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In: Journal of Network and Computer Applications, 2018, vol. 121, p. 59–69
The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of...
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In: Encyclopedia of Big Data Technologies, 2018, p. 1–7
With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al. 2008) or Google’s Knowledge Graph (Google 2014), entities are connected via relations. For example, Bern is capital...
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In: Proceedings of the 26th International Conference on World Wide Web, 2017, p. 627–636
In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this...
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In: World Wide Web, 2017, p. 1–25
Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot...
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In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, USA
For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize...
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In: ACM Trans. Intell. Syst. Technol., 2017, vol. 9, no. 2, p. 20:1–20:28
Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly...
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In: ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16)
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In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI’16)
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