Abstract
As the number of publicly-available datasets are likely to grow, the demand of establishing the links between these datasets is also getting higher and higher. For creating such links we need to match their schemas. Moreover, for using these datasets in meaningful ways, one often needs to match not only two, but several schemas. This matching process establishes a (potentially large) set of attribute correspondences between multiple schemas that constitute a schema matching network. Various commercial and academic schema matching tools have been developed to support this task. However, as the matching is inherently uncertain, the heuristic techniques adopted by these tools give rise to results that are not completely correct. Thus, in practice, a post-matching human expert effort is needed to obtain a correct set of attribute correspondences.
Addressing this problem, our paper demonstrates how to leverage crowdsourcing techniques to validate the generated correspondences. We design validation questions with contextual information that can effectively guide the crowd workers. We analyze how to reduce overall human effort needed for this validation task. Through theoretical and empirical results, we show that by harnessing natural constraints defined on top of the schema matching network, one can significantly reduce the necessary human work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aberer, K., Cudré-Mauroux, P., Hauswirth, M.: Start making sense: The Chatty Web approach for global semantic agreements. JWS, 89–114 (2003)
von Ahn, L.: Human computation. In: DAC, pp. 418–419 (2009)
von Ahn, L., Maurer, B., McMillen, C., Abraham, D., Blum, M.: recaptcha: Human-based character recognition via web security measures. Science, 1465–1468 (2008)
Bernstein, P.A., Madhavan, J., Rahm, E.: Generic Schema Matching, Ten Years Later. PVLDB, 695–701 (2011)
Chen, K.T., Wu, C.C., Chang, Y.C., Lei, C.L.: A crowdsourceable qoe evaluation framework for multimedia content. In: MM, pp. 491–500 (2009)
Cudré-Mauroux, P., Aberer, K., Feher, A.: Probabilistic message passing in peer data management systems. In: ICDE, p. 41 (2006)
Das Sarma, A., Fang, L., Gupta, N., Halevy, A., Lee, H., Wu, F., Xin, R., Yu, C.: Finding related tables. In: SIGMOD, pp. 817–828 (2012)
Dawid, A.P., Skene, A.M.: Maximum likelihood estimation of observer error-rates using the EM algorithm. J. R. Stat. Soc., 20–28 (1979)
Di Lorenzo, G., Hacid, H., Paik, H.Y., Benatallah, B.: Data integration in mashups. In: SIGMOD, pp. 59–66 (2009)
Do, H., Rahm, E.: COMA: a system for flexible combination of schema matching approaches. In: PVLDB, pp. 610–621 (2002)
Duchateau, F., Coletta, R., Bellahsene, Z., Miller, R.J.: (Not) yet another matcher. In: CIKM. pp. 1537–1540 (2009)
Gal, A., Sagi, T.: Tuning the ensemble selection process of schema matchers. JIS, 845–859 (2010)
Gonzalez, H., Halevy, A.Y., Jensen, C.S., Langen, A., Madhavan, J., Shapley, R., Shen, W., Goldberg-Kidon, J.: Google fusion tables: web-centered data management and collaboration. In: SIGMOD, pp. 1061–1066 (2010)
Jeffery, S.R., Franklin, M.J., Halevy, A.Y.: Pay-as-you-go user feedback for dataspace systems. In: SIGMOD, pp. 847–860 (2008)
Lee, Y., Sayyadian, M., Doan, A., Rosenthal, A.S.: eTuner: tuning schema matching software using synthetic scenarios. JVLDB 16, 97–122 (2007)
Madhavan, J., Bernstein, P.A., Doan, A., Halevy, A.: Corpus-based schema matching. In: ICDE, pp. 57–68 (2005)
McCann, R., Shen, W.: Matching schemas in online communities: A web 2.0 approach. In: ICDE, pp. 110–119 (2008)
Nguyen, H., Fuxman, A., Paparizos, S., Freire, J., Agrawal, R.: Synthesizing products for online catalogs. PVLDB, 409–418 (2011)
Parameswaran, A.G., Garcia-Molina, H., Park, H., Polyzotis, N., Ramesh, A., Widom, J.: Crowdscreen: algorithms for filtering data with humans. In: SIGMOD, pp. 361–372 (2012)
Peukert, E., Eberius, J., Rahm, E.: AMC - A framework for modelling and comparing matching systems as matching processes. In: ICDE, pp. 1304–1307 (2011)
Qi, Y., Candan, K.S., Sapino, M.L.: Ficsr: feedback-based inconsistency resolution and query processing on misaligned data sources. In: SIGMOD, pp. 151–162 (2007)
Rahm, E., Bernstein, P.A.: A Survey of Approaches to Automatic Schema Matching. JVLDB, 334–350 (2001)
Sheng, V.S., Provost, F.: Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers. In: SIGKDD, pp. 614–622 (2008)
Smith, K.P., Morse, M., Mork, P., Li, M., Rosenthal, A., Allen, D., Seligman, L., Wolf, C.: The role of schema matching in large enterprises. In: CIDR (2009)
Su, W., Wang, J., Lochovsky, F.: Holistic schema matching for web query interfaces. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 77–94. Springer, Heidelberg (2006)
Yan, T., Kumar, V.: CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones. In: MobiSys, pp. 77–90 (2010)
Zhang, H., Law, E., Miller, R., Gajos, K., Parkes, D., Horvitz, E.: Human computation tasks with global constraints. In: CHI, pp. 217–226 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hung, N.Q.V., Tam, N.T., Miklós, Z., Aberer, K. (2013). On Leveraging Crowdsourcing Techniques for Schema Matching Networks. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7826. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37450-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-37450-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37449-4
Online ISBN: 978-3-642-37450-0
eBook Packages: Computer ScienceComputer Science (R0)