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In this paper, we present L\n            <jats:sc>OG<\/jats:sc>\n            R, a lossy log compression scheme suitable for use in many automated log analytics tools, as well as for human inspection. We formalize and analyze the space\/fidelity trade-off in the context of a broader family of \"pattern\" and \"pattern mixture\" log encodings to which L\n            <jats:sc>OG<\/jats:sc>\n            R belongs. We show through a series of experiments that L\n            <jats:sc>OG<\/jats:sc>\n            R compressed encodings can be created efficiently, come with provable information-theoretic bounds on their accuracy, and outperform state-of-art log summarization strategies.\n          <\/jats:p>","DOI":"10.14778\/3291264.3291265","type":"journal-article","created":{"date-parts":[[2019,2,4]],"date-time":"2019-02-04T13:13:43Z","timestamp":1549286023000},"page":"183-196","source":"Crossref","is-referenced-by-count":5,"title":["Query log compression for workload analytics"],"prefix":"10.14778","volume":"12","author":[{"given":"Ting","family":"Xie","sequence":"first","affiliation":[{"name":"University at Buffalo"}]},{"given":"Varun","family":"Chandola","sequence":"additional","affiliation":[{"name":"University at Buffalo"}]},{"given":"Oliver","family":"Kennedy","sequence":"additional","affiliation":[{"name":"University at Buffalo"}]}],"member":"320","published-online":{"date-parts":[[2018,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142548"},{"key":"e_1_2_1_2_1","volume-title":"VLDB 2000, Proceedings of 26th International Conference on Very Large Data Bases, September 10--14, 2000","author":"Agrawal S.","year":"2000","unstructured":"S. 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