{"title":"PikePlace: Generating Intelligence for Marketplace Datasets","authors":"Shi Qiao, Alekh Jindal","doi":"10.14778/3611540.3611632","DOIUrl":null,"url":null,"abstract":"There is a renewed interest in data marketplaces with cloud data warehouses that make sharing and accessing data on-demand and extremely easy. However, analyzing marketplace datasets is challenge since current tools for creating the data models are manual and slow. In this paper, we propose to demonstrate a learning-based approach to discover, deploy, and optimize data models. We present the resulting system, PikePlace, show an evaluation over Snowflake marketplace and TPC-H datasets, and describe several demonstration scenarios that the audience can play with.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Vldb Endowment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14778/3611540.3611632","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
There is a renewed interest in data marketplaces with cloud data warehouses that make sharing and accessing data on-demand and extremely easy. However, analyzing marketplace datasets is challenge since current tools for creating the data models are manual and slow. In this paper, we propose to demonstrate a learning-based approach to discover, deploy, and optimize data models. We present the resulting system, PikePlace, show an evaluation over Snowflake marketplace and TPC-H datasets, and describe several demonstration scenarios that the audience can play with.
期刊介绍：
The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
