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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2016
عنوان انگلیسی مقاله:
Spatial-Crowd: A Big Data Framework for Efficient Data Visualization
ترجمه فارسی عنوان مقاله:
Spatial-Crowd: A Big Data Framework for Efficient Data Visualization
منبع:
IEEE - 2016 IEEE International Conference on Big Data (Big Data)
نویسنده:
Shahbaz Atta1, Bilal Sadiq1, Akhlaq Ahmad3,5, Sheikh Nasir Saeed1, Emad Felemban
چکیده انگلیسی:
Analyzing and visualizing large datasets generated
by real-time spatio-temporal activities (e.g. vehicle mobility or
large crowd movement) are a very challenging task. Recursive
delays both at middleware and front end applications limit the
of usefulness of the real-time analysis. In this paper, we
present a framework ‘‘Spatial-Crowd’’ that first handles
spatial-temporal data acquisition and processing by scaling up
the middleware components and its infrastructure. Then, it
enables filtering, fixing, enriching and summarising the
acquired dataset, readily available for client interfaces which
usually are not scalable or built to manage such large datasets.
This framework follows published subscriber model and
allows users to subscribe to aggregated data streams instead of
requesting data in real time. The framework is tested with data
generated by a very large simulated dataset and performance
showed a significant data reduction on the client side to
enhance data visualisation.
Keywords Bigdata: Data mining| Visualization| Mobility
قیمت: رایگان
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