Query Optimization for Declarative Crowdsourcing System

Authors(2) :-Nilesh. N. Thorat, A. B. Rajmane

Crowdsourcing is a distributed problem-solving, production model that has emerged in recent years. crowd sourcing is designed to hide the complexities as well as relieve the user from burden of dealing with the crowd data.. The user is requested to pass sql queries to the crowd system to generate the execution plan. Passed query is executed based on the alternative execution query plans in crowd sourcing. Here, CROWDOP a cost-based query optimization approach for declarative crowd sourcing systems is implemented. This considers both cost and latency in query optimization and provides balance between both of them. For this CrowdOp utilizes three types of queries: join queries, selection queries, and complex selection-join queries. At the end results are compared and evaluated.

Authors and Affiliations

Nilesh. N. Thorat
ME CSE Student, Department of Computer Science and Engineering, Ashokrao Mane Group of Institution, Vathar tarf, Maharashtra, India
A. B. Rajmane
Associate Professor, Department of Computer Science and Engineering, Ashokrao Mane Group of Institution, Vathar tarf, Maharashtra, India

Crowdsourcing, query optimization, human intelligence tasks (HIT).

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2016
Date of Publication : 2016-12-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 25-30
Manuscript Number : IJSRST16269
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Nilesh. N. Thorat, A. B. Rajmane, " Query Optimization for Declarative Crowdsourcing System", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 2, Issue 6 , pp.25-30, November-December-2016.
Journal URL : https://ijsrst.com/IJSRST16269
Citation Detection and Elimination     |      | |
  • Fan, M. Lu, B. C. Ooi, W.-C. Tan, and M. Zhang, "A hybrid machine-crowdsourcing system for matching web tables," in Proc. IEEE 30th Int. Conf. Data Eng., 2014, pp. 976–987.
  • J. Franklin, D. Kossmann, T. Kraska, S. Ramesh, and R. Xin, "CrowdDB: Answering queries with crowdsourcing," in Proc.ACM SIGMOD Int. Conf. Manage. Data, 2011, pp. 61–72.
  • -J. Ho, S. Jabbari, and J. W. Vaughan, "Adaptive task assignment for crowdsourced classification," in Proc. 30th Int. Conf. Mach. Language, 2013, vol. 1, pp. 534–542.
  • Park and J. Widom, "Query optimization over crowdsourced data," Proc. VLDB Endowment, vol. 6, no. 10, pp. 781–792, 2013.
  • D. Sharma, A. Parameswaran, H. Garcia-Molina, and A. Halevy, "Crowd-powered find algorithms," in Proc. IEEE 30th Int. Conf. Dta Eng., 2014, pp. 964–975.
  • G. Parameswaran, H. Park, H. Garcia-Molina, N. Polyzotis, J.Widom. Deco: declarative crowdsourcing. In CIKM, pages 1203–1212, 2012.
  • G. Parameswaran, H. Garcia-Molina, H. Park, N. Polyzotis,A. Ramesh, and J. Widom. Crowdscreen: algorithms for filtering data with humans. In SIGMOD Conference, pages 361–372, 2012.
  • D. Sharma, A. Parameswaran, H. Garcia-Molina, and A. Halevy.Crowd-powered find algorithms. In ICDE Conference, 2014.
  • Marcus, E. Wu, D. R. Karger, S. Madden, and R. C. Miller.Human-powered sorts and joins. PVLDB, 5(1):13–24, 2011.
  • " target="_blank"> BibTeX
    |
  • Fan, M. Lu, B. C. Ooi, W.-C. Tan, and M. Zhang, "A hybrid machine-crowdsourcing system for matching web tables," in Proc. IEEE 30th Int. Conf. Data Eng., 2014, pp. 976–987.
  • J. Franklin, D. Kossmann, T. Kraska, S. Ramesh, and R. Xin, "CrowdDB: Answering queries with crowdsourcing," in Proc.ACM SIGMOD Int. Conf. Manage. Data, 2011, pp. 61–72.
  • -J. Ho, S. Jabbari, and J. W. Vaughan, "Adaptive task assignment for crowdsourced classification," in Proc. 30th Int. Conf. Mach. Language, 2013, vol. 1, pp. 534–542.
  • Park and J. Widom, "Query optimization over crowdsourced data," Proc. VLDB Endowment, vol. 6, no. 10, pp. 781–792, 2013.
  • D. Sharma, A. Parameswaran, H. Garcia-Molina, and A. Halevy, "Crowd-powered find algorithms," in Proc. IEEE 30th Int. Conf. Dta Eng., 2014, pp. 964–975.
  • G. Parameswaran, H. Park, H. Garcia-Molina, N. Polyzotis, J.Widom. Deco: declarative crowdsourcing. In CIKM, pages 1203–1212, 2012.
  • G. Parameswaran, H. Garcia-Molina, H. Park, N. Polyzotis,A. Ramesh, and J. Widom. Crowdscreen: algorithms for filtering data with humans. In SIGMOD Conference, pages 361–372, 2012.
  • D. Sharma, A. Parameswaran, H. Garcia-Molina, and A. Halevy.Crowd-powered find algorithms. In ICDE Conference, 2014.
  • Marcus, E. Wu, D. R. Karger, S. Madden, and R. C. Miller.Human-powered sorts and joins. PVLDB, 5(1):13–24, 2011.
  • " target="_blank">RIS
    |
  • Fan, M. Lu, B. C. Ooi, W.-C. Tan, and M. Zhang, "A hybrid machine-crowdsourcing system for matching web tables," in Proc. IEEE 30th Int. Conf. Data Eng., 2014, pp. 976–987.
  • J. Franklin, D. Kossmann, T. Kraska, S. Ramesh, and R. Xin, "CrowdDB: Answering queries with crowdsourcing," in Proc.ACM SIGMOD Int. Conf. Manage. Data, 2011, pp. 61–72.
  • -J. Ho, S. Jabbari, and J. W. Vaughan, "Adaptive task assignment for crowdsourced classification," in Proc. 30th Int. Conf. Mach. Language, 2013, vol. 1, pp. 534–542.
  • Park and J. Widom, "Query optimization over crowdsourced data," Proc. VLDB Endowment, vol. 6, no. 10, pp. 781–792, 2013.
  • D. Sharma, A. Parameswaran, H. Garcia-Molina, and A. Halevy, "Crowd-powered find algorithms," in Proc. IEEE 30th Int. Conf. Dta Eng., 2014, pp. 964–975.
  • G. Parameswaran, H. Park, H. Garcia-Molina, N. Polyzotis, J.Widom. Deco: declarative crowdsourcing. In CIKM, pages 1203–1212, 2012.
  • G. Parameswaran, H. Garcia-Molina, H. Park, N. Polyzotis,A. Ramesh, and J. Widom. Crowdscreen: algorithms for filtering data with humans. In SIGMOD Conference, pages 361–372, 2012.
  • D. Sharma, A. Parameswaran, H. Garcia-Molina, and A. Halevy.Crowd-powered find algorithms. In ICDE Conference, 2014.
  • Marcus, E. Wu, D. R. Karger, S. Madden, and R. C. Miller.Human-powered sorts and joins. PVLDB, 5(1):13–24, 2011.
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