Abstract
Inclusion dependencies (INDs) between databases are assertions of subset-relationships between sets of attributes (dimensions) in two relations. Such dependencies are useful for a number of purposes related to information integration, such as database similarity discovery and foreign key discovery.
An exhaustive approach at discovering INDs between two relations suffers from the dimensionality curse, since the number of potential mappings between the attributes of two relations is exponential in the number of attributes. For this reason, levelwise (Apriori-like) approaches at discovery do not scale beyond relations with 8 to 10 attributes. Approaches modeling the similarity space as graphs or hypergraphs are promising, but also do not scale very well.
This paper discusses approaches to scale discovery algorithms for INDs and some other similarity patterns in databases. The major obstacle to scalability is the exponentially growing size of the data structure representing potential INDs. Therefore, the focus of our solution is on heuristic techniques that reduce the number of IND candidates considered by the algorithm. Despite the use of heuristics, the accuracy of the results is good for real-world data.
Experiments are presented assessing the quality of the discovery results versus the runtime savings. We conclude that the heuristic approach is useful and improves scalability significantly. It is particularly applicable for relations that have attributes with few distinct values.
This work was supported in part by NSF grant #IIS9988776.
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References
Casanova, M.A., Fagin, R., Papadimitriou, C.H.: Inclusion dependencies and their interaction with functional dependencies. In: Proceedings of ACM Conference on Principles of Database Systems (PODS), pp. 171–176 (1982)
Kantola, M., Mannila, H., Räihä, K.J., Siirtola, H.: Discovering functional and inclusion dependencies in relational databases. International J. Of Intelligent Systems 7, 591–607 (1992)
Mannila, H., Räihä, K.J.: Algorithms for inferring functional-dependencies from relations. Data & Knowledge Engineering 12, 83–99 (1994)
de Marchi, F., Lopes, S., Petit, J.M., Toumani, F.: Analysis of existing databases at the logical level: the DBA companion project. SIGMOD Record (ACM Special Interest Group on Management of Data) 32, 47–52 (2003)
Lee, A.J., Nica, A., Rundensteiner, E.A.: The EVE approach: View synchronization in dynamic distributed environments. IEEE Transactions on Knowledge and Data Engineering (TKDE) 14, 931–954 (2002)
Gryz, J.: Query folding with inclusion dependencies. In: Proc. Intl. Conf. on Data Engineering, pp. 126–133. IEEE Computer Society, Los Alamitos (1998)
Calì, A., Lembo, D., Rosati, R.: Query rewriting and answering under constraints in data integration systems. In: Proceedings of IJCAI, pp. 16–21 (2003)
Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB Journal: Very Large Data Bases 10, 334–350 (2001)
Koeller, A.: Integration of Heterogeneous Databases: Discovery of Meta-Information and Maintenance of Schema-Restructuring Views. Ph.D thesis, Worcester Polytechnic Institute, Worcester, MA, USA (2001)
de Marchi, F., Lopes, S., Petit, J.M.: Efficient algorithms for mining inclusion dependencies. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 464–476. Springer, Heidelberg (2002)
Aggarwal, C.C., Yu, P.S.: Online generation of association rules. In: Proceedings of IEEE International Conference on Data Engineering, pp. 402–411 (1998)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1, 241–258 (1997)
Koeller, A., Rundensteiner, E.A.: Discovery of high-dimensional inclusion dependencies. In: Proceedings of IEEE International Conference on Data Engineering, Bangalore, India, pp. 683–685. IEEE, Los Alamitos (2003)
de Marchi, F., Petit, J.M.: Zigzag: A new algorithm for mining large inclusion dependencies in databases. In: 3rd Intl. Conf. on Data Mining, Melbourne, Florida, pp. 27–34. IEEE, Los Alamitos (2003)
Mitra, P., Wiederhold, G., Jannink, J.: Semi-automatic integration of knowledge sources. In: Proc. of the 2nd Int. Conf. On Information Fusion (FUSION 1999), Sunnyvale, California (1999)
Beneventano, D., Bergamaschi, S., Castano, S., et al.: Information integration: The MOMIS project demonstration. In: International Conference on Very Large Data Bases, pp. 611–614 (2000)
Koeller, A., Rundensteiner, E.A.: Heuristic Strategies for Inclusion Dependency Discovery. In: Proceedings of 3rd International Conference on Ontologies, Databases and Applications of Semantics (ODBASE), pp. 891–908 (2004)
Koeller, A., Rundensteiner, E.A.: Discovery of high-dimensional inclusion dependencies. Technical Report WPI-CS-TR-02-15, Worcester Polytechnic Institute, Dept. of Computer Science (2002)
Demetrovics, J., Thi, V.D.: Some remarks on generating armstrong and inferring functional dependencies relation. Acta Cybernetica 12, 167–180 (1995)
Rice, J.A.: Mathematical Statistics and Data Analysis, 2nd edn. Duxbury Press, Boston (1995)
Lim, W., Harrison, J.: Discovery of constraints from data for information system reverse engineering. In: Proc. of Australian Software Engineering Conference (ASWEC 1997), Sydney, Australia (1997)
Zaki, M.J.: Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering (TKDE) 12, 372–390 (2000)
Chandra, A.K., Vardi, M.Y.: The implication problem for functional and inclusion dependencies is undecidable. SIAM J. Comput. 3, 671–677 (1985)
Mitchell, J.C.: Inference rules for functional and inclusion dependencies. In: Proceedings of ACM Symposium on Principles of Database Systems, Atlanta, Georgia, pp. 58–69 (1983)
Mannino, M.V., Chu, P., Sager, T.: Statistical profile estimation in database systems. ACM Computing Surveys 20 (1988)
Hon, W.C., Zhang, Z., Zhou, N.: Statistical inference of unknown attribute values in databases. In: Proceedings of International Conference on Information and Knowledge Management, pp. 21–30 (1993)
Batini, C., Lenzerini, M., Navathe, S.: A comparative analysis of methodologies for database schema integration. ACM Computing Surveys 18, 323–364 (1986)
Larson, J.A., Navathe, S.B., Elmasri, R.: A theory of attribute equivalence in databases with application to schema integration. IEEE Transactions on Software Engineering 15, 449–463 (1989)
Li, W., Clifton, C.: SemInt: A tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data and Knowledge Engineering 33(1), 49–84 (2000)
Doan, A., Domingos, P., Halevy, A.: Learning source description for data integration. In: Proceedings of the Third International Workshop on the Web and Databases (WebDB), Dallas, pp. 81–86 (2000)
Kang, J., Naughton, J.F.: On schema matching with opaque column names and data values. Proceedings of SIGMOD pp. 205–216 (2003)
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Koeller, A., Rundensteiner, E.A. (2006). Heuristic Strategies for the Discovery of Inclusion Dependencies and Other Patterns. In: Spaccapietra, S., Atzeni, P., Chu, W.W., Catarci, T., Sycara, K.P. (eds) Journal on Data Semantics V. Lecture Notes in Computer Science, vol 3870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11617808_7
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