图 书 馆
EmbedJoin：通过嵌入进行高效编辑相似性连接EmbedJoin: Efficient Edit Similarity Joins via Embeddings |
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| 课程网址: | http://videolectures.net/kdd2017_zhang_embedjoin/ |
| 主讲教师: | 张浩宇 |
| 开课单位: | 印第安纳大学 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We study the problem of edit similarity joins, where given a set of strings and a threshold value K, we want to output all pairs of strings whose edit distances are at most K. Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems. This problem has been studied extensively in the literature. However, we have observed that all the existing algorithms fall short on long strings and large distance thresholds. In this paper we propose an algorithm named EmbedJoin which scales very well with string length and distance threshold. Our algorithm is built on the recent advance of metric embeddings for edit distance, and is very different from all of the previous approaches. We demonstrate via an extensive set of experiments that EmbedJoin significantly outperforms the previous best algorithms on long strings and large distance thresholds. |
| 关 键 词: | 高效编辑; 编辑相似性连接; 原始运算符 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-12-26：wujk |
| 最后编审: | 2023-12-26：wujk |
| 阅读次数: | 12 |