TY - GEN
T1 - Decomposition-Based Approximation of Time Series Data with Max-Error Guarantees
AU - Ruan, Boyu
AU - Hua, Wen
AU - Zhang, Ruiyuan
AU - Zhou, Xiaofang
N1 - Funding Information:
Acknowledgment. This research is partially supported by the Australian Research Council (Grant No.DP170101172) and the Queensland Government (Grant No.AQRF12516).
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - With the growing popularity of IoT nowadays, tremendous amount of time series data at high resolution is being generated, transmitted, stored, and processed by modern sensor networks in different application domains, which naturally incurs extensive storage and computation cost in practice. Data compression is the key to resolve such challenge, and various compression techniques, either lossless or lossy, have been proposed and widely adopted in industry and academia. Although existing approaches are generally successful, we observe a unique characteristic in certain time series data, i.e., significant periodicity and strong randomness, which leads to poor compression performance using existing methods and hence calls for a specifically designed compression mechanism that can utilise the periodic and stochastic patterns at the same time. To this end, we propose a decomposition-based compression algorithm which divides the original time series into several components reflecting periodicity and randomness respectively, and then approximates each component accordingly to guarantee overall compression ratio and maximum error. We conduct extensive evaluation on a real world dataset, and the experimental results verify the superiority of our proposals compared with current state-of-the-art methods.
AB - With the growing popularity of IoT nowadays, tremendous amount of time series data at high resolution is being generated, transmitted, stored, and processed by modern sensor networks in different application domains, which naturally incurs extensive storage and computation cost in practice. Data compression is the key to resolve such challenge, and various compression techniques, either lossless or lossy, have been proposed and widely adopted in industry and academia. Although existing approaches are generally successful, we observe a unique characteristic in certain time series data, i.e., significant periodicity and strong randomness, which leads to poor compression performance using existing methods and hence calls for a specifically designed compression mechanism that can utilise the periodic and stochastic patterns at the same time. To this end, we propose a decomposition-based compression algorithm which divides the original time series into several components reflecting periodicity and randomness respectively, and then approximates each component accordingly to guarantee overall compression ratio and maximum error. We conduct extensive evaluation on a real world dataset, and the experimental results verify the superiority of our proposals compared with current state-of-the-art methods.
KW - Decomposition-based algorithm
KW - High periodicity
KW - Max-error guarantee
KW - Strong randomness
KW - Time series compression
UR - http://www.scopus.com/inward/record.url?scp=85030682448&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68155-9_6
DO - 10.1007/978-3-319-68155-9_6
M3 - Conference article published in proceeding or book
AN - SCOPUS:85030682448
SN - 9783319681542
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 71
EP - 82
BT - Databases Theory and Applications - 28th Australasian Database Conference, ADC 2017, Proceedings
A2 - Xiao, Xiaokui
A2 - Cao, Xin
A2 - Huang, Zi
PB - Springer Verlag
T2 - 28th Australasian Database Conference, ADC 2017
Y2 - 25 September 2017 through 28 September 2017
ER -