CrowdDB: answering queries with crowdsourcing
CrowdDB: answering queries with crowdsourcing
复制标题
DOI：
10.1145/1989323.1989331
复制
发表时间：
2011-06
期刊：
影响因子：
--
通讯作者：
M. Franklin;Donald Kossmann;Tim Kraska;Sukriti Ramesh;Reynold Xin
中科院分区：
文献类型：
--
作者：
M. Franklin;Donald Kossmann;Tim Kraska;Sukriti Ramesh;Reynold Xin
有些问题不能只靠机器回答。处理这样的查询需要人工输入，以提供数据库中缺失的信息，执行计算上困难的功能，以及基于模糊标准来匹配、排序或聚合结果。CrowdDB通过众包使用人工输入来处理数据库系统和搜索引擎都无法充分回答的查询。它使用SQL作为提出复杂查询的语言和建模数据的方法。虽然CrowdDB利用了传统数据库系统的许多方面，但也存在重要的差异。从概念上讲，一个主要的变化是，传统的封闭世界的假设，查询处理不成立的人类输入。从实现的角度来看，需要面向人的查询操作符来请求、整合和清理众包数据。此外，性能和成本取决于许多新的因素，包括工人的亲和力，培训，疲劳，动机和位置。我们描述了CrowdDB的设计，报告使用Amazon Mechanical Turk的初始实验集，并概述了众包查询处理系统开发中未来工作的重要途径。
Some queries cannot be answered by machines only. Processing such queries requires human input for providing information that is missing from the database, for performing computationally difficult functions, and for matching, ranking, or aggregating results based on fuzzy criteria. CrowdDB uses human input via crowdsourcing to process queries that neither database systems nor search engines can adequately answer. It uses SQL both as a language for posing complex queries and as a way to model data. While CrowdDB leverages many aspects of traditional database systems, there are also important differences. Conceptually, a major change is that the traditional closed-world assumption for query processing does not hold for human input. From an implementation perspective, human-oriented query operators are needed to solicit, integrate and cleanse crowdsourced data. Furthermore, performance and cost depend on a number of new factors including worker affinity, training, fatigue, motivation and location. We describe the design of CrowdDB, report on an initial set of experiments using Amazon Mechanical Turk, and outline important avenues for future work in the development of crowdsourced query processing systems.