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Abstract
To provide compressed storage for large amounts of time series data, we present a new strategy for data deduplication. Rather than attempting to deduplicate entire data chunks, we employ a generalized approach, where each chunk is split into a part worth deduplicating and a part that must be stored directly. This simple principle enables a greater compression of the often similar, non-identical, chunks of time series data than is the case for classic deduplication, while keeping benefits such as scalability, robustness, and on-the-fly storage, retrieval, and search for chunks. We analyze the method's theoretical performance, and argue that our method can asymptotically approach the entropy limit for some data configurations. To validate the method's practical merits, we finally show that it is competitive when compared to popular universal compression algorithms on the MIT-BIH ECG Compression Test Database.
Original language | English |
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Title of host publication | 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings |
Publisher | IEEE |
Publication date | Dec 2019 |
Article number | 9013957 |
ISBN (Electronic) | 978-1-7281-0962-6 |
DOIs | |
Publication status | Published - Dec 2019 |
Event | 2019 IEEE Global Communications Conference - Waikoloa, HI, USA, Waikoloa, United States Duration: 9 Dec 2019 → 13 Dec 2019 https://globecom2019.ieee-globecom.org/ |
Conference
Conference | 2019 IEEE Global Communications Conference |
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Location | Waikoloa, HI, USA |
Country/Territory | United States |
City | Waikoloa |
Period | 09/12/2019 → 13/12/2019 |
Internet address |
Keywords
- Data compression
- Deduplication
- Storage
- Time Series Data
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