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add the ALECE paper
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paul356 committed May 22, 2024
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11 changes: 6 additions & 5 deletions _org/2024-05-17-may-papers.org
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| Title | Authors | Synthesis | Publisher | Keywords |
|------------------------------------------------------------------------------+---------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+----------|
| The R*-tree: An Efficient and Robust AccessMethod for Points and Rectangles+ | Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, and Bernhard Seeger | R-Tree is a popular tree structure for managing spatial shapes. In the origional paper [[http://www-db.deis.unibo.it/courses/SI-LS/papers/Gut84.pdf][Gut84]]. The origion algorithm is framed that minimum area increase is set as the only criteria. But it is showed in some cases the origional algorithm will generate bad results. Reconsider the criterias of a R-Tree with optimal retrieval performance this paper introduces a new algorithm for steps ChooseSubTree and QudraticSplit. The result show it can improve the retrieval performance at the cost of slightly increasing the insert cost. | SIGMOD 90 | R-Tree |
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| Title | Authors | Synthesis | Publisher | Keywords |
|----------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+-----------------------------------|
| The R*-tree: An Efficient and Robust AccessMethod for Points and Rectangles+ | Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, and Bernhard Seeger | R-Tree is a popular tree structure for managing spatial shapes. In the origional paper [[http://www-db.deis.unibo.it/courses/SI-LS/papers/Gut84.pdf][Gut84]]. The origion algorithm is framed that minimum area increase is set as the only criteria. But it is showed in some cases the origional algorithm will generate bad results. Reconsider the criterias of a R-Tree with optimal retrieval performance this paper introduces a new algorithm for steps ChooseSubTree and QudraticSplit. The result show it can improve the retrieval performance and robustness at the cost of slightly increasing the insert cost. | SIGMOD 90 | R-Tree |
| ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads | Pengfei Li, Wenqing Wei, Rong Zhu, Bolin Ding, Jingren Zhou, Hua Lu | ALECE is another learned based cardinality estimator which learns from true cardinalities. It takes featurized data distribution and queries as input. With two attention structures, one self attention for data features and one cross attention between data features and query features, it can achieve much better estimate than competitors. | VLDB 2023 | Cardinality Estimation, Attention |
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5 changes: 3 additions & 2 deletions _posts/2024-05-17-may-papers.md
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| Title | Authors | Synthesis | Publisher | Keywords |
| The R\*-tree: An Efficient and Robust AccessMethod for Points and Rectangles+ | Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, and Bernhard Seeger | R-Tree is a popular tree structure for managing spatial shapes. In the origional paper [Gut84](http://www-db.deis.unibo.it/courses/SI-LS/papers/Gut84.pdf). The origion algorithm is framed that minimum area increase is set as the only criteria. But it is showed in some cases the origional algorithm will generate bad results. Reconsider the criterias of a R-Tree with optimal retrieval performance this paper introduces a new algorithm for steps ChooseSubTree and QudraticSplit. The result show it can improve the retrieval performance at the cost of slightly increasing the insert cost. | SIGMOD 90 | R-Tree |
| Title | Authors | Synthesis | Publisher | Keywords |
| The R\*-tree: An Efficient and Robust AccessMethod for Points and Rectangles+ | Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, and Bernhard Seeger | R-Tree is a popular tree structure for managing spatial shapes. In the origional paper [Gut84](http://www-db.deis.unibo.it/courses/SI-LS/papers/Gut84.pdf). The origion algorithm is framed that minimum area increase is set as the only criteria. But it is showed in some cases the origional algorithm will generate bad results. Reconsider the criterias of a R-Tree with optimal retrieval performance this paper introduces a new algorithm for steps ChooseSubTree and QudraticSplit. The result show it can improve the retrieval performance and robustness at the cost of slightly increasing the insert cost. | SIGMOD 90 | R-Tree |
| ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads | Pengfei Li, Wenqing Wei, Rong Zhu, Bolin Ding, Jingren Zhou, Hua Lu | ALECE is another learned based cardinality estimator which learns from true cardinalities. It takes featurized data distribution and queries as input. With two attention structures, one self attention for data features and one cross attention between data features and query features, it can achieve much better estimate than competitors. | VLDB 2023 | Cardinality Estimation, Attention |

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