CDB: A Crowd-Powered Database System

Guoliang Li, Chengliang Chai, Ju Fan, Xueping Weng, Jian Li, Yudian Zheng, Yuanbing Li, Xiang Yu, Xiaohang Zhang, Haitao Yuan

Research output: Contribution to journalConference articlepeer-review

25 Citations (Scopus)

Abstract

Crowd-powered database systems can leverage the crowd's ability to address machine-hard problems, e.g., data integration. Existing crowdsourcing systems adopt the traditional tree model to select a good query plan. However, the tree model can optimize the I/O cost but cannot optimize the monetary cost, latency and quality, which are three important optimization goals in crowdsourcing. To address this limitation, we demonstrate CDB, a crowd-powered database system. CDB proposes a new graph-based model that adopts a fine-grained tuple-level optimization model which significantly outperforms existing coarse-grained tree-based optimization models. Moreover, CDB provides a unified frame- work to simultaneously optimize the monetary cost, quality and latency. We have deployed CDB on well-known crowd- sourcing platforms and users can easily use our system to deploy their applications. We will demonstrate how to use CDB to address real-world applications, including web table integration and entity collection.

Original languageEnglish
Pages (from-to)1926-1929
Number of pages4
JournalProceedings of the VLDB Endowment
Volume11
Issue number12
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil
Duration: 27 Aug 201831 Aug 2018

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