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 language | English |
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Pages (from-to) | 1926-1929 |
Number of pages | 4 |
Journal | Proceedings of the VLDB Endowment |
Volume | 11 |
Issue number | 12 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Event | 44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil Duration: 27 Aug 2018 → 31 Aug 2018 |