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 framework 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.

Crowdsourced selection asks the crowd to select entities that satisfy a query condition, e.g., selecting the photos of people wearing sunglasses from a given set of photos. Existing studies focus on a single query predicate and in this paper we study the crowdsourced selection problem on multi-attribute data, e.g., selecting the female photos with dark eyes and wearing sunglasses. A straightforward method asks the crowd to answer every entity by checking every predicate in the query. Obviously, this method involves huge monetary cost. Instead, we can select an optimized predicate order and ask the crowd to answer the entities following the order. Since if an entity does not satisfy a predicate, we can prune this entity without needing to ask other predicates and thus this method can reduce the cost. There are two challenges in finding the optimized predicate order. The first is how to detect the predicate order and the second is to capture correlation among different predicates. To address this problem, we propose predicate order based framework to reduce monetary cost. Firstly, we define an expectation tree to store selectivities on predicates and estimate the best predicate order. In each iteration, we estimate the best predicate order from the expectation tree, and then choose a predicate as a question to ask the crowd. After getting the result of the current predicate, we choose next predicate to ask until we get the result. We will update the expectation tree using the answer obtained from the crowd and continue to the next iteration. We also study the problem of answering multiple queries simultaneously, and reduce its cost using the correlation between queries. Finally, we propose a confidence based method to improve the quality. The experiment result shows that our predicate order based algorithm is effective and can reduce cost significantly compared with baseline approaches.

Crowdsourcing database systems have been proposed to leverage crowd-powered operations to encapsulate the complexities of interacting with the crowd. Existing systems suffer from two major limitations. Firstly, in order to optimize a query, they often adopt the traditional tree model to select an optimized table-level join order. However, the tree model provides a coarse-grained optimization, which generates the same order for different joined tuples and limits the optimization potential that different joined tuples can be optimized by different orders. Secondly, they mainly focus on optimizing the monetary cost. In fact, there are three optimization goals (i.e., smaller monetary cost, lower latency, and higher quality) in crowdsourcing, and it calls for a system to enable multi-goal optimization.