Anfrage-getriebener Wissenstransfer zur Unterstuetzung von Datenanalysten

TL;DR

This paper presents a novel approach to enhance knowledge sharing among data scientists in large organizations by extending data management systems to facilitate collaboration and improve data source discovery without disrupting existing workflows.

Contribution

It introduces a system extension that formalizes and shares collective knowledge from query logs to support data source discovery and integration across teams.

Findings

Supports data source discovery through formalized knowledge extraction

Enhances collaboration without altering existing workflows

Improves data integration efficiency across teams

Abstract

In larger organizations, multiple teams of data scientists have to integrate data from heterogeneous data sources as preparation for data analysis tasks. Writing effective analytical queries requires data scientists to have in-depth knowledge of the existence, semantics, and usage context of data sources. Once gathered, such knowledge is informally shared within a specific team of data scientists, but usually is neither formalized nor shared with other teams. Potential synergies remain unused. We therefore introduce a novel approach which extends data management systems with additional knowledge-sharing capabilities to facilitate user collaboration without altering established data analysis workflows. Relevant collective knowledge from the query log is extracted to support data source discovery and incremental data integration. Extracted knowledge is formalized and provided at query time.