{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T19:08:04Z","timestamp":1779131284046,"version":"3.51.4"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2024YFF0617702"],"award-info":[{"award-number":["2024YFF0617702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["U22A2025, 62232007, U23A20309"],"award-info":[{"award-number":["U22A2025, 62232007, U23A20309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"crossref","award":["B16009"],"award-info":[{"award-number":["B16009"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"crossref"}]},{"name":"CCF-ApsaraDB Research Fund","award":["CCF-Aliyun2024006"],"award-info":[{"award-number":["CCF-Aliyun2024006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"<jats:p>\n                    To address the dual challenges of data transmission and storage in Internet of Things (IoT) systems, the development of efficient streaming lossless compression algorithms for floating-point data has become a critical research focus. Existing XOR-based floating-point compression algorithms adopt a fixed reference selection strategy by using the immediate predecessor value as the reference. Usually, this choice is not the best, as earlier values in the data stream often provide more similar and effective references. Based on this observation, we propose dynamically searching previous values to identify the reference value that yields the best compression results for the current value. To this end, we present\n                    <jats:bold>Kangaroo,<\/jats:bold>\n                    an efficient lossless compression algorithm for floating-point time series that implements a dynamic reference selection. Our approach begins by establishing optimal reference selection criteria aimed at minimizing the number of encoded bits, introducing a pruning-based encoding strategy to reduce encoding overhead. We further propose a dynamic search strategy that selects reference values from an extended historical window, skipping historical data that generates suboptimal XOR results. This strategy enables precise identification of the suitable reference value while improving search efficiency. Additionally, we design a bit-flip-based erasure strategy to maximize trailing zeros, thereby comprehensively enhancing compression performance. Extensive experiments on 26 datasets demonstrate that\n                    <jats:italic toggle=\"yes\">Kangaroo<\/jats:italic>\n                    outperforms all baseline methods across all evaluation metrics. Most notably, compared to state-of-the-art streaming compression algorithms, our algorithm achieves a\n                    <jats:bold>23.2%<\/jats:bold>\n                    average improvement (peaking at\n                    <jats:bold>98.0%<\/jats:bold>\n                    ) in compression ratio, with compression and decompression speeds averaging 2.13\u00d7 and 2.21\u00d7 (reaching up to 11.75\u00d7 and 5.24\u00d7) of the baseline, respectively. This novel solution establishes comprehensive performance leadership in floating-point time-series compression, and has been applied to the Lindorm database on Alibaba Cloud since 2024, managing tens of petabytes of Internet of Vehicles (IoV) data.\n                  <\/jats:p>","DOI":"10.1145\/3802076","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-26","source":"Crossref","is-referenced-by-count":0,"title":["Kangaroo: Efficient Lossless Floating-Point Compression via Dynamic Reference Selection"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5339-8750","authenticated-orcid":false,"given":"Shuo","family":"Li","sequence":"first","affiliation":[{"name":"Northeastern University, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6184-4771","authenticated-orcid":false,"given":"Xiaochun","family":"Yang","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9704-4272","authenticated-orcid":false,"given":"Chunhui","family":"Shen","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1301-8969","authenticated-orcid":false,"given":"Yutong","family":"Han","sequence":"additional","affiliation":[{"name":"Dalian Minzu University, Dalian, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5183-5798","authenticated-orcid":false,"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3657-8926","authenticated-orcid":false,"given":"Lingdu","family":"Kong","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2694-1023","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3486-517X","authenticated-orcid":false,"given":"Feibo","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"issue":"1","key":"e_1_2_1_1_1","first-page":"1013","article-title":"A distributed nosql-based trajectory data management system","volume":"35","author":"Li Ruiyuan","year":"2023","unstructured":"Ruiyuan Li, Huajun He, Rubin Wang, Sijie Ruan, Tianfu He, Jie Bao, Junbo Zhang, Liang Hong, and Yu Zheng. Trajmesa: A distributed nosql-based trajectory data management system. IEEE Transactions on Knowledge and Data Engineering, 35(1):1013-1027, 2023.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_1_2_1","volume-title":"Lindorm database","author":"Cloud Alibaba","year":"2024","unstructured":"Alibaba Cloud. Lindorm database, 2024."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/3685800.3685831"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611540.3611559"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1977.1055714"},{"key":"e_1_2_1_6_1","volume-title":"Brotli: A general-purpose data compressor. ACM Transactions on Information Systems (TOIS), 37(1):1-30","author":"Alakuijala Jyrki","year":"2018","unstructured":"Jyrki Alakuijala, Andrea Farruggia, Paolo Ferragina, Eugene Kliuchnikov, Robert Obryk, Zoltan Szabadka, and Lode Vandevenne. Brotli: A general-purpose data compressor. ACM Transactions on Information Systems (TOIS), 37(1):1-30, 2018."},{"key":"e_1_2_1_7_1","volume-title":"Zstd github repository from facebook","author":"Collet Y","year":"2016","unstructured":"Y Collet. Zstd github repository from facebook, 2016. Retrieved May 4, 2025 from https:\/\/github.com\/facebook\/zstd."},{"key":"e_1_2_1_8_1","volume-title":"xz file format","author":"Utils Contributors XZ","year":"2023","unstructured":"XZ Utils Contributors. The. xz file format, 2023. Retrieved May 4, 2025 from https:\/\/tukaani.org\/xz\/xz-file-format.txt."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3725353"},{"key":"e_1_2_1_10_1","first-page":"532","volume-title":"2024 Data Compression Conference (DCC)","author":"Shi Yang","year":"2024","unstructured":"Yang Shi, Xiangyu Zou, Xinyu Chen, Sian Jin, Dingwen Tao, Cai Deng, Yufan Chen, and Wen Xia. Machete: An efficient lossy floating-point compressor designed for time series databases. In 2024 Data Compression Conference (DCC), pages 532-541, Snowbird, UT, USA, 2024. IEEE, IEEE."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/ICDCS60910.2024.00060","volume-title":"2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)","author":"Wilkins Grant","year":"2024","unstructured":"Grant Wilkins, Sheng Di, Jon C. Calhoun, Zilinghan Li, Kibaek Kim, Robert Underwood, Richard Mortier, and Franck Cappello. Fedsz: Leveraging error-bounded lossy compression for federated learning communications. In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), pages 577-588, Jersey City, NJ, USA, 2024. IEEE."},{"key":"e_1_2_1_12_1","first-page":"1041","volume-title":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","author":"Lu Bing","year":"2023","unstructured":"Bing Lu, Yida Li, Junqi Wang, Huizhang Luo, and Kenli Li. Zfp-x: Efficient embedded coding for accelerating lossy floating point compression. In 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 1041-1050, St. Petersburg, FL, USA, 2023. IEEE, IEEE."},{"key":"e_1_2_1_13_1","first-page":"117","volume-title":"2019 International Conference on Field-Programmable Technology (ICFPT)","author":"Sun Gongjin","year":"2019","unstructured":"Gongjin Sun and Sang-Woo Jun. Zfp-v: Hardware-optimized lossy floating point compression. In 2019 International Conference on Field-Programmable Technology (ICFPT), pages 117-125, Tianjin, China, 2019. IEEE, IEEE."},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1109\/IPDPS47924.2020.00065","volume-title":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","author":"Underwood Robert","year":"2020","unstructured":"Robert Underwood, Sheng Di, Jon C. Calhoun, and Franck Cappello. Fraz: A generic high-fidelity fixed-ratio lossy compression framework for scientific floating-point data. In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 567-577, New Orleans, LA, USA, 2020. IEEE."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824078"},{"issue":"11","key":"e_1_2_1_16_1","doi-asserted-by":"crossref","first-page":"3058","DOI":"10.14778\/3551793.3551852","article-title":"efficient lossless floating point compression for time series databases","volume":"15","author":"Liakos Panagiotis","year":"2022","unstructured":"Panagiotis Liakos, Katia Papakonstantinopoulou, and Yannis Kotidis. Chimp: efficient lossless floating point compression for time series databases. Proceedings of the VLDB Endowment, 15(11):3058-3070, 2022.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/3587136.3587149"},{"key":"e_1_2_1_18_1","volume-title":"Erasing-based lossless compression method for streaming floating-point time series. arXiv preprint arXiv:2306.16053","author":"Li Ruiyuan","year":"2023","unstructured":"Ruiyuan Li, Zheng Li, Yi Wu, Chao Chen, Songtao Guo, Ming Zhang, and Yu Zheng. Erasing-based lossless compression method for streaming floating-point time series. arXiv preprint arXiv:2306.16053, 2023."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3554999"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626717"},{"key":"e_1_2_1_21_1","first-page":"395","volume-title":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems","volume":"1","author":"Azami Noushin","year":"2025","unstructured":"Noushin Azami, Alex Fallin, and Martin Burtscher. Efficient lossless compression of scientific floating-point data on cpus and gpus. In Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1, pages 395-409, New York, NY, USA, 2025. Association for Computing Machinery."},{"key":"e_1_2_1_22_1","first-page":"1","volume-title":"Proceedings of the international conference for high performance computing, networking, storage and analysis","author":"Knorr Fabian","year":"2021","unstructured":"Fabian Knorr, Peter Thoman, and Thomas Fahringer. ndzip-gpu: efficient lossless compression of scientific floating-point data on gpus. In Proceedings of the international conference for high performance computing, networking, storage and analysis, pages 1-14, New York, NY, USA, 2021. Association for Computing Machinery."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2012.194"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2006.143"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476305"},{"key":"e_1_2_1_26_1","volume-title":"Proc. ACM Manag. Data, 2(6)","author":"Yao Yuanyuan","year":"2024","unstructured":"Yuanyuan Yao, Lu Chen, Ziquan Fang, Yunjun Gao, Christian S. Jensen, and Tianyi Li. Camel: Efficient compression of floating-point time series. Proc. ACM Manag. Data, 2(6), December 2024."},{"key":"e_1_2_1_27_1","volume-title":"Barometric pressure (dp1.00004.001)","author":"National Ecological Observatory Network (NEON).","year":"2025","unstructured":"National Ecological Observatory Network (NEON). Barometric pressure (dp1.00004.001), 2025."},{"key":"e_1_2_1_28_1","unstructured":"InfluxData. Influxdb 2.0 sample data 2023. Retrieved May 4 2025 from https:\/\/github.com\/influxdata\/influxdb2-sample-data."},{"key":"e_1_2_1_29_1","volume-title":"Daily electricity price and demand data","author":"Kozlov Alex","year":"2020","unstructured":"Alex Kozlov. Daily electricity price and demand data, 2020."},{"key":"e_1_2_1_30_1","volume-title":"Highway merge interaction dataset","year":"2025","unstructured":"Mirror-Traffic. Highway merge interaction dataset, 2025."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304839"},{"key":"e_1_2_1_32_1","volume-title":"London weather data","author":"Assessment European Climate","year":"2021","unstructured":"European Climate Assessment & Dataset (ECA&D). London weather data, 2021. Retrieved May 4, 2025 from https:\/\/www.kaggle.com\/datasets\/emmanuelfwerr\/london-weather-data."},{"key":"e_1_2_1_33_1","volume-title":"Dataset of legitimate IoT data. https:\/\/www.data.gouv.fr\/en\/datasets\/dataset-of-legitimate-iot-data\/#\/resources","author":"SudParis T\u00e9l\u00e9com","year":"2022","unstructured":"T\u00e9l\u00e9com SudParis. Dataset of legitimate IoT data. https:\/\/www.data.gouv.fr\/en\/datasets\/dataset-of-legitimate-iot-data\/#\/resources, 2022. Accessed: May 4, 2025."},{"key":"e_1_2_1_34_1","volume-title":"Online retail dataset. https:\/\/archive.ics.uci.edu\/dataset\/352\/onlineretail","author":"Chen Daqing","year":"2012","unstructured":"Daqing Chen and UCI Machine Learning Repository. Online retail dataset. https:\/\/archive.ics.uci.edu\/dataset\/352\/onlineretail, 2012. Accessed: May 4, 2025."},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the VLDB Endowment, 17(7)","author":"Chen Xinyu","year":"2021","unstructured":"Xinyu Chen, Jiannan Tian, Ian Beaver, Cynthia Freeman, Yan Yan, Jianguo Wang, and Dingwen Tao. Fcbench: Cross-domain benchmarking of lossless compression for floating-point data. Proceedings of the VLDB Endowment, 17(7), 2021."},{"key":"e_1_2_1_36_1","volume-title":"Zenodo","year":"2020","unstructured":"Spring. Financial data set used in infore project. Zenodo, 2020. Data set."},{"key":"e_1_2_1_37_1","volume-title":"Blockchair database dumps","year":"2025","unstructured":"Blockchair. Blockchair database dumps, 2025. Retrieved May 4, 2025 from https:\/\/gz.blockchair.com\/bitcoin\/transactions\/."},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the Workshop on Testing Database Systems, DBTest '18","author":"Vogelsgesang Adrian","year":"2018","unstructured":"Adrian Vogelsgesang, Michael Haubenschild, Jan Finis, Alfons Kemper, Viktor Leis, Tobias Muehlbauer, Thomas Neumann, and Manuel Then. Get real: How benchmarks fail to represent the real world. In Proceedings of the Workshop on Testing Database Systems, DBTest '18, New York, NY, USA, 2018. Association for Computing Machinery."},{"key":"e_1_2_1_39_1","volume-title":"Global food prices database (wfp)","author":"World Food","year":"2021","unstructured":"World Food Programme (WFP). Global food prices database (wfp), 2021."},{"key":"e_1_2_1_40_1","volume-title":"Global video game sales & ratings","author":"Poutanen Timo","year":"2023","unstructured":"Timo Poutanen. Global video game sales & ratings, 2023. Retrieved May 4, 2025 from https:\/\/www.kaggle.com\/datasets\/thedevastator\/global-video-game-sales-ratings."},{"key":"e_1_2_1_41_1","volume-title":"Ssd and hdd benchmarks","author":"Software PassMark","year":"2022","unstructured":"PassMark Software. Ssd and hdd benchmarks, 2022."},{"key":"e_1_2_1_42_1","unstructured":"Transaction Processing Performance Council(TPC). Tpcx-bb 2023. Online."},{"key":"e_1_2_1_43_1","unstructured":"Google. Snappy | a fast compressor\/decompressor 2023. Retrieved May 4 2025 from https:\/\/github.com\/google\/snappy."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/266800.266824"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2006.143"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/DCC.2006.35"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/DCC.2007.44"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2008.131"},{"issue":"2","key":"e_1_2_1_49_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3589263","article-title":"Efficient columnar compression for data lakes","volume":"1","author":"Kuschewski Maximilian","year":"2023","unstructured":"Maximilian Kuschewski, David Sauerwein, Adnan Alhomssi, and Viktor Leis. Btrblocks: Efficient columnar compression for data lakes. Proceedings of the ACM on Management of Data, 1(2):1-26, 2023.","journal-title":"Proceedings of the ACM on Management of Data"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.14778\/3598581.3598587"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964179.1964189"},{"key":"e_1_2_1_52_1","first-page":"389","volume-title":"Proceedings of the 2015 IEEE International Conference on Cluster Computing, CLUSTER '15","author":"Yang Annie","year":"2015","unstructured":"Annie Yang, Hari Mukka, Farbod Hesaaraki, and Martin Burtscher. Mpc: A massively parallel compression algorithm for scientific data. In Proceedings of the 2015 IEEE International Conference on Cluster Computing, CLUSTER '15, page 381\u2013389, USA, 2015. IEEE Computer Society."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476224"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/DCC50243.2021.00023"},{"key":"e_1_2_1_55_1","volume-title":"Proceedings of the 21st International Workshop on Data Management on New Hardware, DaMoN '25","author":"Hepkema Sven","year":"2025","unstructured":"Sven Hepkema, Azim Afroozeh, Charlotte Felius, Peter Boncz, and Stefan Manegold. G-alp: Rethinking light-weight encodings for gpus. In Proceedings of the 21st International Workshop on Data Management on New Hardware, DaMoN '25, New York, NY, USA, 2025. Association for Computing Machinery."}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3802076","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:35:56Z","timestamp":1779129356000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3802076"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5,18]]}},"alternative-id":["10.1145\/3802076"],"URL":"https:\/\/doi.org\/10.1145\/3802076","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,18]]}}}