We Can Query More than We Can Tell: Facilitating Collaboration Through Query-Driven Knowledge-Sharing

被引：1

作者：

Wahl, Andreas M. ^{[1
]}

Herbst, Sebastian ^{[1
]}

Endler, Gregor ^{[1
]}

Lenz, Richard ^{[1
]}

Schwab, Peter K. ^{[1
]}

机构：

[1] FAU Erlangen Nurnberg, Erlangen, Germany

来源：

CSCW'17: COMPANION OF THE 2017 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING | 2017年

关键词：

collaborative data science; knowledge sharing; data integration; data source discovery;

D O I：

10.1145/3022198.3026327

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We introduce Query-driven Knowledge-Sharing Systems (QKSS), which extend data management systems with knowledge-sharing capabilities to facilitate collaboration among different teams of data scientists. Relevant tacit knowledge about data sources is extracted from SQL query logs and externalized to support data source discovery and data integration. By studying this collaborative knowledge, data scientists are enabled to formulate effective analytical queries over unfamiliar data sources.

引用

页码：335 / 338

页数：4

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