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Titchy: Online Time-series Compression with Random Access for the Internet of Things

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Titchy: Online Time-series Compression with Random Access for the Internet of Things. / Vestergaard, Rasmus; Zhang, Qi; Sipos, Marton et al.

In: IEEE Internet of Things Journal, Vol. 8, No. 24, 2021, p. 17568-17583.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Vancouver

Vestergaard R, Zhang Q, Sipos M, Lucani Rötter DE. Titchy: Online Time-series Compression with Random Access for the Internet of Things. IEEE Internet of Things Journal. 2021;8(24):17568-17583. Epub 2021 May. doi: 10.1109/JIOT.2021.3081868

Author

Vestergaard, Rasmus ; Zhang, Qi ; Sipos, Marton et al. / Titchy: Online Time-series Compression with Random Access for the Internet of Things. In: IEEE Internet of Things Journal. 2021 ; Vol. 8, No. 24. pp. 17568-17583.

Bibtex

@article{258a1584311c44e7b82bdb599a9cf4cf,

title = "Titchy: Online Time-series Compression with Random Access for the Internet of Things",

abstract = "We introduce Titchy, which is a compression method for time-series data generated by the Internet of Things. Our proposed method is flexible and has several advantages when applied in the IoT ecosystem: 1) it is able to compress even when only a small amount of memory can be allocated to it; 2) it compresses data in tiny chunks, so it introduces very little latency during online operation and thus, enables frequent and timely updates with compressed data; and 3) it facilitates efficient data storage and retrieval by enabling low-cost random access to the compressed data, eliminating the need to decompress large chunks when only a small amount of data is requested. To evaluate each of these advantages, we have implemented the compressor and conducted extensive experiments with long-term real-world data captured over days or weeks. We also present results for seven state-of-the-art compression methods to act as a baseline. Our evaluation shows that Titchy not only outperforms all seven on random access capability and for frequent transmissions but also provides great compression ratios as well as high compression and decompression speeds.",

keywords = "Channel coding, Cloud computing, Data compression, Decoding, Dictionaries, Entropy, Internet of Things, Servers, data storage systems, generalized data deduplication, sensor data.",

author = "Rasmus Vestergaard and Qi Zhang and Marton Sipos and {Lucani R{\"o}tter}, {Daniel Enrique}",

year = "2021",

doi = "10.1109/JIOT.2021.3081868",

language = "English",

volume = "8",

pages = "17568--17583",

journal = "IEEE Internet of Things Journal",

issn = "2327-4662",

publisher = "Institute of Electrical and Electronics Engineers",

number = "24",

}

RIS

TY - JOUR

T1 - Titchy: Online Time-series Compression with Random Access for the Internet of Things

AU - Vestergaard, Rasmus

AU - Zhang, Qi

AU - Sipos, Marton

AU - Lucani Rötter, Daniel Enrique

PY - 2021

Y1 - 2021

N2 - We introduce Titchy, which is a compression method for time-series data generated by the Internet of Things. Our proposed method is flexible and has several advantages when applied in the IoT ecosystem: 1) it is able to compress even when only a small amount of memory can be allocated to it; 2) it compresses data in tiny chunks, so it introduces very little latency during online operation and thus, enables frequent and timely updates with compressed data; and 3) it facilitates efficient data storage and retrieval by enabling low-cost random access to the compressed data, eliminating the need to decompress large chunks when only a small amount of data is requested. To evaluate each of these advantages, we have implemented the compressor and conducted extensive experiments with long-term real-world data captured over days or weeks. We also present results for seven state-of-the-art compression methods to act as a baseline. Our evaluation shows that Titchy not only outperforms all seven on random access capability and for frequent transmissions but also provides great compression ratios as well as high compression and decompression speeds.

AB - We introduce Titchy, which is a compression method for time-series data generated by the Internet of Things. Our proposed method is flexible and has several advantages when applied in the IoT ecosystem: 1) it is able to compress even when only a small amount of memory can be allocated to it; 2) it compresses data in tiny chunks, so it introduces very little latency during online operation and thus, enables frequent and timely updates with compressed data; and 3) it facilitates efficient data storage and retrieval by enabling low-cost random access to the compressed data, eliminating the need to decompress large chunks when only a small amount of data is requested. To evaluate each of these advantages, we have implemented the compressor and conducted extensive experiments with long-term real-world data captured over days or weeks. We also present results for seven state-of-the-art compression methods to act as a baseline. Our evaluation shows that Titchy not only outperforms all seven on random access capability and for frequent transmissions but also provides great compression ratios as well as high compression and decompression speeds.

KW - Channel coding

KW - Cloud computing

KW - Data compression

KW - Decoding

KW - Dictionaries

KW - Entropy

KW - Internet of Things

KW - Servers

KW - data storage systems

KW - generalized data deduplication

KW - sensor data.

U2 - 10.1109/JIOT.2021.3081868

DO - 10.1109/JIOT.2021.3081868

M3 - Journal article

VL - 8

SP - 17568

EP - 17583

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 24

ER -