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.

@inproceedings{franklin2011crowddb, author = {Franklin, Michael J. and Kossmann, Donald and Kraska, Tim and Ramesh, Sukriti and Xin, Reynold}, title = {CrowdDB: answering queries with crowdsourcing}, booktitle = {Proceedings of the 2011 international conference on Management of data}, publisher = {ACM}, address = {New York, NY, USA}, year = {2011}, pages = {61--72}, url = {http://doi.acm.org/10.1145/1989323.1989331}, doi = {10.1145/1989323.1989331}, isbn = {978-1-4503-0661-4}, keywords = {cirg, collective, computation, crowddb, crowdsourcing, database, human, intelligence, social}, abstract = {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.} }

%0 = inproceedings %A = Franklin, Michael J. and Kossmann, Donald and Kraska, Tim and Ramesh, Sukriti and Xin, Reynold %B = Proceedings of the 2011 international conference on Management of data %C = New York, NY, USA %D = 2011 %I = ACM %T = CrowdDB: answering queries with crowdsourcing %U = http://doi.acm.org/10.1145/1989323.1989331

Human computation is a technique that makes use of human abilities for computation to solve problems. The human computation problems are the problems those computers are not good at solving but are trivial for humans. In this paper, we give a survey of various human computation systems which are categorized into initiatory human computation, distributed human computation and social game-based human computation with volunteers, paid engineers and online players. For the existing large number of social games, some previous works defined various types of social games, but the recent developed social games cannot be categorized based on the previous works. In this paper, we define the categories and the characteristics of social games which are suitable for all existing ones. Besides, we present a survey on the performance aspects of human computation system. This paper gives a better understanding on human computation system.

@inproceedings{yuen2009survey, author = {Yuen, Man-Ching and Chen, Ling-Jyh and King, I.}, title = {A Survey of Human Computation Systems}, booktitle = {Proceedings of the International Conference on Computational Science and Engineering, CSE '09}, year = {2009}, volume = {4}, pages = {723--728}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5283450&tag=1}, doi = {10.1109/CSE.2009.395}, keywords = {cirg, collective, computation, human, intelligence, social, survey, toread}, abstract = {Human computation is a technique that makes use of human abilities for computation to solve problems. The human computation problems are the problems those computers are not good at solving but are trivial for humans. In this paper, we give a survey of various human computation systems which are categorized into initiatory human computation, distributed human computation and social game-based human computation with volunteers, paid engineers and online players. For the existing large number of social games, some previous works defined various types of social games, but the recent developed social games cannot be categorized based on the previous works. In this paper, we define the categories and the characteristics of social games which are suitable for all existing ones. Besides, we present a survey on the performance aspects of human computation system. This paper gives a better understanding on human computation system.} }

%0 = inproceedings %A = Yuen, Man-Ching and Chen, Ling-Jyh and King, I. %B = Proceedings of the International Conference on Computational Science and Engineering, CSE '09 %D = 2009 %T = A Survey of Human Computation Systems %U = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5283450&tag=1

@article{brand06, author = {Brand, Matthew}, title = {Fast Low-Rank Modifications of the Thin Singular Value Decomposition}, journal = {Linear Algebra and Its Applications}, year = {2006}, volume = {415}, number = {1}, pages = {20--30}, keywords = {computation, fast, svd, toread} }

%0 = article %A = Brand, Matthew %D = 2006 %T = Fast Low-Rank Modifications of the Thin Singular Value Decomposition

@inproceedings{stumme00fast, author = {Stumme, G. and Taouil, R. and Bastide, Y. and Pasquier, N. and Lakhal, L.}, title = {Fast Computation of Concept Lattices Using Data Mining Techniques}, editor = {Bouzeghoub, M. and Klusch, M. and Nutt, W. and Sattler, U.}, booktitle = {Proc. 7th Intl. Workshop on Knowledge Representation Meets Databases}, year = {2000}, note = {http://ceur-ws.org/Vol-29. Part of testumme02computing}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/KRDB00.pdf}, keywords = {2000, algorithm, algorithms, analysis, closed, computation, concept, condensed, fca, formal, iceberg, itemsets, lattices, myown, representations} }

%0 = inproceedings %A = Stumme, G. and Taouil, R. and Bastide, Y. and Pasquier, N. and Lakhal, L. %B = Proc. 7th Intl. Workshop on Knowledge Representation Meets Databases %D = 2000 %T = Fast Computation of Concept Lattices Using Data Mining Techniques %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2000/KRDB00.pdf

@book{Golub1996, author = {Golub, Gene H. and Loan, Charles F. Van}, title = {Matrix Computations}, publisher = {The Johns Hopkins University Press}, year = {1996}, edition = {3rd}, keywords = {computation, matrix} }

%0 = book %A = Golub, Gene H. and Loan, Charles F. Van %D = 1996 %I = The Johns Hopkins University Press %T = Matrix Computations