\n","children":[{"type":"text","text":""}]},{"type":"p","children":[{"type":"text","text":"This paper aims to be a brief introduction to the design and analysis of\nworst-case optimal join algorithms. We discuss the key techniques for proving\nruntime and output size bounds.","italic":true}]},{"type":"p","children":[{"type":"text","text":"Author: Hung Q. Ngo. 2018."}]},{"type":"p","children":[{"type":"text","text":"In Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of\nDatabase Systems (SIGMOD/PODS ‘18).","italic":true}]},{"type":"p","children":[{"type":"text","text":"Worst-case optimal join algorithms are the class of join algorithms whose\nruntime match the worst-case output size of a given join query. While the first\nprovably worst-case optimal join algorithm was discovered relatively recently,\nthe techniques and results surrounding these algorithms grow out of decades of\nresearch from a wide range of areas, intimately connecting graph theory,\nalgorithms, information theory, constraint satisfaction, database theory, and\ngeometric inequalities. These ideas are not just paperware: in addition to\nacademic project implementations, two variations of such algorithms are the\nwork-horse join algorithms of commercial database and data analytics engines."}]},{"type":"p","children":[{"type":"text","text":"Read the PDF:\n"},{"type":"a","url":"https://arxiv.org/pdf/1803.09930.pdf","title":null,"children":[{"type":"text","text":"Worst-Case Optimal Join Algorithms: Techniques, Results and Open Problems"}]}]}],"_content_source":{"queryId":"src/content/resources/worst-case-optimal-join-algorithms-techniques-results-and-open-problems.mdx","path":["resource","body"]}},"_content_source":{"queryId":"src/content/resources/worst-case-optimal-join-algorithms-techniques-results-and-open-problems.mdx","path":["resource"]}}},"errors":null,"query":"\n query resource($relativePath: String!) {\n resource(relativePath: $relativePath) {\n ... on Document {\n _sys {\n filename\n basename\n breadcrumbs\n path\n relativePath\n extension\n }\n id\n }\n 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Worst-Case Optimal Join Algorithms: Techniques, Results and Open Problems · RelationalAI
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Worst-Case Optimal Join Algorithms: Techniques, Results and Open Problems
This paper aims to be a brief introduction to the design and analysis of
worst-case optimal join algorithms. We discuss the key techniques for proving
runtime and output size bounds.
Author: Hung Q. Ngo. 2018.
In Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of
Database Systems (SIGMOD/PODS ‘18).
Worst-case optimal join algorithms are the class of join algorithms whose
runtime match the worst-case output size of a given join query. While the first
provably worst-case optimal join algorithm was discovered relatively recently,
the techniques and results surrounding these algorithms grow out of decades of
research from a wide range of areas, intimately connecting graph theory,
algorithms, information theory, constraint satisfaction, database theory, and
geometric inequalities. These ideas are not just paperware: in addition to
academic project implementations, two variations of such algorithms are the
work-horse join algorithms of commercial database and data analytics engines.