308
edits
Seminar on Internet Technologies (Winter 2017/2018): Difference between revisions
Seminar on Internet Technologies (Winter 2017/2018) (view source)
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| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266] | | [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266] | ||
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| '''Traffic Data Analysis --A survey ''' | |||
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning. | |||
| [Shichang Ding -- shichang.ding@informatik.uni-goettingen.de] | |||
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf] | |||
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| '''Fuctional Zone Discovery inside Cities -- A survey''' | |||
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc. | |||
| [Shichang Ding -- shichang.ding@informatik.uni-goettingen.de] | |||
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf] | |||
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| '''Human Trajectory Clustering -- A survey''' | |||
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra- | |||
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior, | |||
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering. | |||
| [Shichang Ding -- shichang.ding@informatik.uni-goettingen.de] | |||
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y] | |||
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