Abstract
In this paper, we present a data mining approach to challenges in the matching and integration of heterogeneous datasets. In particular, we propose solutions to two problems that arise in combining information from different results of scientific research. The first problem, attribute matching, involves discovery of correspondences among distinct numeric-typed summary features (“attributes”) that are used to characterize datasets that have been collected and analyzed in different research labs. The second problem, cluster matching, involves discovery of matchings between patterns across datasets. We treat both of these problems together as a multi-objective optimization problem. A multi-objective simulated annealing algorithm is described to find the optimal solution. The utility of this approach is demonstrated in a series of experiments using synthetic and realistic datasets that are designed to simulate heterogeneous data from different sources.
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
Bae, E., Bailey, J., Dong, G.: A Clustering Comparison Measure Using Density Profiles and Its Application to The Discovery tf Alternate Clusterings. Data Min. Knowl. Discov. 21, 427–471 (2010), http://dx.doi.org/10.1007/s10618-009-0164-z
Dhamankar, R., Lee, Y., Doan, A., Halevy, A., Domingos, P.: iMAP: Discovering Complex Semantic Matches between Database Schemas. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. ACM Press (2004)
Dien, J.: The ERP PCA Toolkit: An Open Source Program for Advanced Statistical Analysis of Event-Related Potential Data. Journal of Neuroscience Methods 187(1), 138–145 (2010), http://www.sciencedirect.com/science/article/B6T04-4Y0KWB2-4/2/3c0e7b36b475b8d0e9a72c7b868a7dcd
Doan, A., Domingos, P., Levy, A.Y.: Learning Source Description for Data Integration. In: WebDB (Informal Proceedings), pp. 81–86 (2000), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.9378
Fred, A.L., Jain, A.K.: Robust Data Clustering. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 128 (2003)
Frishkoff, G.A., Frank, R.M., Rong, J., Dou, D., Dien, J., Halderman, L.K.: A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns. Computational Intelligence and Neuroscience (CIN), Special Issue, EEG/MEG Analysis and Signal Processing 7(3), 1–13 (2007)
Hamers, L., Hemeryck, Y., Herweyers, G., Janssen, M., Keters, H., Rousseau, R., Vanhoutte, A.: Similarity Measures In Scientometric Research: The Jaccard Index Versus Salton’s Cosine Formula. Inf. Process. Manage. 25, 315–318 (1989), http://portal.acm.org/citation.cfm?id=67223.67231
Kuhn, H.W.: The Hungarian Method for The Assignment Problem. Naval Research Logistic Quarterly 2, 83–97 (1955)
Larson, J.A., Navathe, S.B., Elmasri, R.: A Theory of Attributed Equivalence in Databases with Application to Schema Integration. IEEE Trans. Softw. Eng. 15, 449–463 (1989), http://portal.acm.org/citation.cfm?id=63379.63387
Li, W.S., Clifton, C.: Semint: A Tool for Identifying Attribute Correspondences in Heterogeneous Databases Using Neural Networks (2000)
Liu, H., Frishkoff, G., Frank, R., Dou, D.: Ontology-Based Mining of Brainwaves: A Sequence Similarity Technique for Mapping Alternative Features in Event-Related Potentials (ERP) Data. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS, vol. 6119, pp. 43–54. Springer, Heidelberg (2010)
Rahm, E., Bernstein, P.A.: A Survey of Approaches to Automatic Schema Matching. VLDB Journal 10 (2001)
Rand, W.M.: Objective Criteria for the Evaluation of Clustering Methods. Journal of the American Statistical Association 66(336), 846–850 (1971), http://dx.doi.org/10.2307/2284239
Sheth, A.P., Larson, J.A., Cornelio, A., Navathe, S.B.: A Tool for Integrating Conceptual Schemas and User Views. In: Proceedings of the Fourth International Conference on Data Engineering, pp. 176–183. IEEE Computer Society, Washington, DC, USA (1988), http://portal.acm.org/citation.cfm?id=645473.653395
Suman, B.: Simulated annealing based multiobjective algorithm and their application for system reliability. Engin. Optim., 391–416 (2003)
Suman, B., Kumar, P.: A survey of simulated annealing as a tool for single¡/b¿ and multiobjective optimization. Journal of the Operational Research Society 57, 1143–1160 (2006)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study, pp. 292–301. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, H., Dou, D. (2011). Breaking the Deadlock: Simultaneously Discovering Attribute Matching and Cluster Matching with Multi-Objective Simulated Annealing. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2011. OTM 2011. Lecture Notes in Computer Science, vol 7045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25106-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-25106-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25105-4
Online ISBN: 978-3-642-25106-1
eBook Packages: Computer ScienceComputer Science (R0)