PikePlace:为市场数据集生成智能

IF 2.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611632

Shi Qiao, Alekh Jindal

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引用次数: 0

摘要

人们对数据市场重新产生了兴趣，云数据仓库使得按需共享和访问数据变得非常容易。然而，分析市场数据集是一个挑战，因为当前创建数据模型的工具是手动的，而且速度很慢。在本文中，我们建议演示一种基于学习的方法来发现、部署和优化数据模型。我们展示了最终的系统PikePlace，展示了对雪花市场和TPC-H数据集的评估，并描述了几个演示场景，供观众使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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PikePlace: Generating Intelligence for Marketplace Datasets

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.

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来源期刊

Proceedings of the Vldb Endowment Computer Science-General Computer Science

CiteScore

7.70

自引率

0.00%

发文量

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.