Open AccessProceedings Article
Answering Queries using Humans, Algorithms and Databases
Aditya Parameswaran,Neoklis Polyzotis +1 more
- pp 160-166
TLDR
The design of the first declarative language involving human-computable functions, standard relational operators, as well as algorithmic computation is described, which can act as a roadmap for new area of data management research where human computation is routinely used in data analytics.Abstract:
For some problems, human assistance is needed in addition to automated (algorithmic) computation. In sharp contrast to existing data management approaches, where human input is either ad-hoc or is never used, we describe the design of the first declarative language involving human-computable functions, standard relational operators, as well as algorithmic computation. We consider the challenges involved in optimizing queries posed in this language, in particular, the tradeoffs between uncertainty, cost and performance, as well as combination of human and algorithmic evidence. We believe that the vision laid out in this paper can act as a roadmap for a new area of data management research where human computation is routinely used in data analytics.read more
Citations
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Crowdsourcing systems on the World-Wide Web
TL;DR: The practice of crowdsourcing is transforming the Web and giving rise to a new field of inquiry called "crowdsourcing", which aims to provide real-time information about events in a democratic manner.
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CrowdDB: answering queries with crowdsourcing
TL;DR: The design of CrowdDB is described, a major change is that the traditional closed-world assumption for query processing does not hold for human input, and important avenues for future work in the development of crowdsourced query processing systems are outlined.
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GeoCrowd: enabling query answering with spatial crowdsourcing
Leyla Kazemi,Cyrus Shahabi +1 more
TL;DR: This paper introduces a taxonomy for spatial crowdsourcing, and focuses on one class of this taxonomy, in which workers send their locations to a centralized server and thereafter the server assigns to every worker his nearby tasks with the objective of maximizing the overall number of assigned tasks.
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CDAS: a crowdsourcing data analytics system
TL;DR: A quality-sensitive answering model is introduced, which guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs, and effectively reduces the processing cost while maintaining the required query answer quality.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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Pattern Recognition and Machine Learning (Information Science and Statistics)
TL;DR: Looking for competent reading resources?
Active Learning Literature Survey
TL;DR: This report provides a general introduction to active learning and a survey of the literature, including a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date.
Journal ArticleDOI
Designing games with a purpose
Luis von Ahn,Laura Dabbish +1 more
TL;DR: Data generated as a side effect of game play also solves computational problems and trains AI algorithms.