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Users can also connect the platform to their own existing database and evaluate indexes for custom workloads. Our platform comprises multiple state-of-the-art index selection approaches, which can be used as baselines for new index selection proposals. Further, we present an application for thoroughly analyzing the selected database indexes. One can observe which indexes are used for which queries and their effect on processing costs. Also, it is possible to adapt the resulting index selections (i.e., add, remove, or change an index) and observe the impact. In this process, the application helps to understand the effects of indexes, improve index selections, and craft new index selection approaches.<\/jats:p>","DOI":"10.14778\/3685800.3685860","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4301-4304","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Looking Deeply into the Magic Mirror: An Interactive Analysis of Database Index Selection Approaches"],"prefix":"10.14778","volume":"17","author":[{"given":"Stefan","family":"Halfpap","sequence":"first","affiliation":[{"name":"BIFOLD, TU Berlin, Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Kossmann","sequence":"additional","affiliation":[{"name":"Snowflake Inc., Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rainer","family":"Schlosser","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, Potsdam, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Volker","family":"Markl","sequence":"additional","affiliation":[{"name":"BIFOLD, TU Berlin, DFKI, Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2304510.2304522"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066184"},{"key":"e_1_2_1_3_1","unstructured":"Surajit Chaudhuri and Vivek Narasayya. 2020. Anytime Algorithm of Database Tuning Advisor for Microsoft SQL Server. (2020). https:\/\/www.microsoft.com\/en-us\/research\/publication\/anytime-algorithm-of-database-tuning-advisor-for-microsoft-sql-server accessed: July 17 2024."},{"volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB). 146--155","author":"Chaudhuri Surajit","key":"e_1_2_1_4_1","unstructured":"Surajit Chaudhuri and Vivek R. Narasayya. 1997. An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. 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Introducing Dexter, the Automatic Indexer for Postgres. https:\/\/medium.com\/@ankane\/introducing-dexter-the-automatic-indexer-for-postgres-5f8fa8b28f27, accessed: July 17, 2024."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407832"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT). 2:155--2:168","author":"Kossmann Jan","year":"2022","unstructured":"Jan Kossmann, Alexander Kastius, and Rainer Schlosser. 2022. SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning. 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