Senbazuru: a prototype spreadsheet database management system

Spreadsheets have become a critical data management tool, but they lack explicit relational metadata, making it difficult to join or integrate data across multiple spreadsheets. Because spreadsheet data are widely available on a huge range of topics, a tool that allows easy spreadsheet integration w...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings of the VLDB Endowment 2013-08, Vol.6 (12), p.1202-1205
Hauptverfasser: Chen, Zhe, Cafarella, Michael, Chen, Jun, Prevo, Daniel, Zhuang, Junfeng
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Spreadsheets have become a critical data management tool, but they lack explicit relational metadata, making it difficult to join or integrate data across multiple spreadsheets. Because spreadsheet data are widely available on a huge range of topics, a tool that allows easy spreadsheet integration would be hugely beneficial for a variety of users. We demonstrate that Senbazuru, a prototype spreadsheet database management system (SSDBMS), is able to extract relational information from spreadsheets. By doing so, it opens up opportunities for integration among spreadsheets and with other relational sources. Senbazuru allows users to search for relevant spreadsheets in a large corpus, probabilistically constructs a relational version of the data, and offers several relational operations over the resulting extracted data (including joins to other spreadsheet data). Our demonstration is available on two clients: a JavaScript-rich Web site and a touch interface on the iPad. During the demo, Senbazuru will allow VLDB participants to search spreadsheets, extract relational data from them, and apply relational operators such as select and join.
ISSN:2150-8097
2150-8097
DOI:10.14778/2536274.2536276