Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12838
Title: VALID : A web framework for visual analytics of large streaming data
Authors: Li, C
Baciu, G 
Keywords: Big data
Dynamic visualization
Streaming data
Visual analytics
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014, 2015, 7011313, p. 686-692 How to cite?
Abstract: Visual analytics of increasingly large data sets has become a challenge for traditional in-memory and off-line algorithms as well as in the cognitive process of understanding features at various scales of resolution. In this paper, we attempt a new web-based framework for the dynamic visualization of large data. The framework is based on the idea that no physical device can ever catch up to the analytical demand and the physical requirements of large data. Thus, we adopt a data streaming generator model that serializes the original data into multiple streams of data that can be contained on current hardware. Thus, the scalability of the visual analytics of large data is inherent in the streaming architecture supported by our platform. The platform is based on the traditional server-client model. However, the platform is enhanced by effective analytical methods that operate on data streams, such as binned points and bundling lines that reduce and enhance large streams of data for effective interactive visualization. We demonstrate the effectiveness of our framework on different types of large datasets.
Description: 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014, Beijing, 24-26 September 2014
URI: http://hdl.handle.net/10397/12838
ISBN: 9781479965137
DOI: 10.1109/TrustCom.2014.89
Appears in Collections:Conference Paper

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