32nd International Conference on Scientific and Statistical Database Management最新文献

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Selectivity Estimation for Relation-Tree Joins 关系树连接的选择性估计
32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-05-09 DOI: 10.1145/3400903.3400921
Chao Zhang, Jiaheng Lu
{"title":"Selectivity Estimation for Relation-Tree Joins","authors":"Chao Zhang, Jiaheng Lu","doi":"10.1145/3400903.3400921","DOIUrl":"https://doi.org/10.1145/3400903.3400921","url":null,"abstract":"Estimating the join selectivity is a crucial problem in many aspects of query processing, such as query optimization and query refinement. Selectivity estimation has been extensively studied for the relational joins in SQL queries and structural joins in path-oriented queries. However, as leading databases have supported the multi-model data management on relational and tree-structured data together, a new problem has arisen: the existing estimation techniques mainly work for a single model but not for the heterogeneous situation due to the cross-model joins. A straightforward combination of existing estimators cannot provide a satisfactory estimation quality. This paper studies the problem of selectivity estimation for cross-model joins with relational and tree-structured data. Our estimator is based on the Kernel Density Estimation (KDE) model, which is a statistical approach using a data sample to approximate multivariate probability distribution. KDE has been successfully applied in relational databases to estimate the selectivity of range and join query. In this work, we propose an estimation method called location-value estimation (LVE) model based on KDE, which considers both value joins and structural joins in relational and tree-structured data. To boost the estimation efficiency in large data samples, we further propose the max-min approximation (MMA) and grid-based approximation (GBA) models to approximate the KDE contribution. Extensive experiments on four real and synthetic datasets demonstrate the effectiveness, efficiency, and scalability of our techniques.","PeriodicalId":334018,"journal":{"name":"32nd International Conference on Scientific and Statistical Database Management","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115343848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
An Algebraic Approach for High-level Text Analytics 高级文本分析的代数方法
32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-05-03 DOI: 10.1145/3400903.3400926
Xiuwen Zheng, Amarnath Gupta
{"title":"An Algebraic Approach for High-level Text Analytics","authors":"Xiuwen Zheng, Amarnath Gupta","doi":"10.1145/3400903.3400926","DOIUrl":"https://doi.org/10.1145/3400903.3400926","url":null,"abstract":"Text analytical tasks like word embedding, phrase mining and topic modeling, are placing increasing demands as well as challenges to existing database management systems. In this paper, we provide a novel algebraic approach based on associative arrays. Our data model and algebra can bring together relational operators and text operators, which enables interesting optimization opportunities for hybrid data sources that have both relational and textual data. We demonstrate its expressive power in text analytics using several real-world tasks.","PeriodicalId":334018,"journal":{"name":"32nd International Conference on Scientific and Statistical Database Management","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123387870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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