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Context-aware database has drawn increasing attention from both industry and academia recently by taking users current situation and environment into consideration. However, most of the literature focus on individual context, overlooking the team users. In this paper, we investigate how to integrate team context into database query process to help the users get top-ranked database tuples and make the team more competitive. We introduce naive and optimized query algorithm to select the suitable records and show that they output the same results while the latter is more computational efficient. Extensive empirical studies are conducted to evaluate the query approaches and demonstrate their effectiveness and efficiency.
In this paper, we propose a 2D based partition method for solving the problem of Ranking under Team Context(RTC) on datasets without a priori. We first map the data into 2D space using its minimum and maximum value among all dimensions. Then we const
Localization of street objects from images has gained a lot of attention in recent years. We propose an approach to improve asset geolocation from street view imagery by enhancing the quality of the metadata associated with the images using Structure
Open Cloud Robot Table Organization Challenge (OCRTOC) is one of the most comprehensive cloud-based robotic manipulation competitions. It focuses on rearranging tabletop objects using vision as its primary sensing modality. In this extended abstract,
Considerable effort has been made to increase the scale of Linked Data. However, an inevitable problem when dealing with data integration from multiple sources is that multiple different sources often provide conflicting objects for a certain predica
It has been repeatedly conjectured that the brain retrieves statistical regularities from stimuli. Here we present a new statistical approach allowing to address this conjecture. This approach is based on a new class of stochastic processes driven by