Titlebook: Big Data Analytics in the Social and Ubiquitous Context; 5th International Wo Martin Atzmueller,Alvin Chin,Christoph Trattner Conference pr

MEEK

Context-Aware Location Prediction,ation of vehicles. We use five contextual features related to either the object environment or its current movement data: current location; object velocity; day of the week; weather conditions; and traffic congestion in the area. Our algorithm incorporates these context features into its trajectory-

暗語

https://doi.org/10.1007/978-94-009-8179-9esults in a partially annotated corpus that is likely to contain unannotated entities. To learn from such partially annotated data, we devise two simple extensions of hidden Markov models and structural perceptrons. Empirically, we observe that using the automatically generated data leads to more ac

吞噬

https://doi.org/10.1007/978-94-009-8179-9crucially – the reported timeframe for the occurrence of the event discussed – whether it be in the past, present, or future. Tweets (Twitter posts) that mention an event to occur reportedly in the future prove to be important predictors. These signals are enhanced by cross referencing with the frag

確認(rèn)

,Géométrie diophantienne multiprojective,d by a nonnegative matrix factorization algorithm (NMF). NMF is used to learn dictionaries of usages that can be exploited in order to characterize user mobility and station patterns. The relevance of the extracted dictionaries is then assessed by using them to cluster users and stations. This analy

單調(diào)性

Medicaid

Big Data Analytics in the Social and Ubiquitous Context5th International Wo

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辭職

摘要

Modify