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International Conference on Big Data Analytics

BDA 2019: Big Data Analytics pp 324–340Cite as

Local Temporal Compression for (Globally) Evolving Spatial Surfaces

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11932))

Abstract

The advances in the Internet of Things (IoT) paradigm have enabled generation of large volumes of data from multiple domains, capturing the evolution of various physical and social phenomena of interest. One of the consequences of such enormous data generation is that it needs to be stored, processed and queried – along with having the answers presented in an intuitive manner. A number of techniques have been proposed to alleviate the impact of the sheer volume of the data on the storage and processing overheads, along with bandwidth consumption – and, among them, the most dominant is compression. In this paper, we consider a setting in which multiple geographically dispersed data sources are generating data streams – however, the values from the discrete locations are used to construct a representation of continuous (time-evolving) surface. We have used different compression techniques to reduce the size of the raw measurements in each location, and we analyzed the impact of the compression on the quality of approximating the evolution of the shapes corresponding to a particular phenomenon. Specifically, we use the data from discrete locations to construct a TIN (triangulated irregular networks), which evolves over time as the measurements in each locations change. To analyze the global impact of the different compression techniques that are applied locally, we used different surface distance functions between raw-data TINs and compressed data TINs. We provide detailed discussions based on our experimental observations regarding the corresponding (compression method, distance function) pairs.

X. Teng—Research supported by NSF grant III 1823267.

P. Giri—Research supported by NSF grant CNS 182367.

J. Sun—Research supported by NSF-REU grant 018522

G. Trajcevski—Research supported by NSF grants III-1823279 and CNS-1823267.

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Notes

  1. 1.

    The very first implementation dating back to 1973s, due to W. Randolph Franklin.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, 50014, USA

    Xu Teng, Prabin Giri, Matthew Dwyer, Jidong Sun & Goce Trajcevski

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  1. Xu Teng
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  2. Prabin Giri
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  3. Matthew Dwyer
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  4. Jidong Sun
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  5. Goce Trajcevski
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Corresponding author

Correspondence to Goce Trajcevski .

Editor information

Editors and Affiliations

  1. Missouri University of Science and Technology, Rolla, MO, USA

    Sanjay Madria

  2. Harbin Institute of Technology, Shenzhen, China

    Philippe Fournier-Viger

  3. Ahmedabad University, Ahmedabad, India

    Sanjay Chaudhary

  4. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

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Teng, X., Giri, P., Dwyer, M., Sun, J., Trajcevski, G. (2019). Local Temporal Compression for (Globally) Evolving Spatial Surfaces. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_19

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  • DOI: https://doi.org/10.1007/978-3-030-37188-3_19

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